CN111862214A - Computer equipment positioning method and device, computer equipment and storage medium - Google Patents
Computer equipment positioning method and device, computer equipment and storage medium Download PDFInfo
- Publication number
- CN111862214A CN111862214A CN202010744438.6A CN202010744438A CN111862214A CN 111862214 A CN111862214 A CN 111862214A CN 202010744438 A CN202010744438 A CN 202010744438A CN 111862214 A CN111862214 A CN 111862214A
- Authority
- CN
- China
- Prior art keywords
- data set
- head
- data
- local map
- top view
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/70—Determining position or orientation of objects or cameras
- G06T7/73—Determining position or orientation of objects or cameras using feature-based methods
- G06T7/74—Determining position or orientation of objects or cameras using feature-based methods involving reference images or patches
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/10—Image acquisition modality
- G06T2207/10028—Range image; Depth image; 3D point clouds
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
- Y02D10/00—Energy efficient computing, e.g. low power processors, power management or thermal management
Landscapes
- Engineering & Computer Science (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Length Measuring Devices By Optical Means (AREA)
Abstract
The invention discloses a computer equipment positioning method, a computer equipment positioning device, computer equipment and a storage medium, wherein the method comprises the following steps: acquiring a top view data set, a head up data set and a positioning data set of computer equipment; respectively determining the top view matching degree of the top view data set and the local map and the head-up matching degree of the head-up data set and the local map based on the positioning data set; determining a target pose in the local map based on the top view matching degree and the level view matching degree; the top view data set and the head up data set are added to the local map based on the target pose, and the position of the computer device is determined in the global map according to the local map. The method and the device realize accurate positioning of the computer equipment, reduce the influence of environmental change on positioning accuracy, reduce the probability of positioning failure of the computer equipment and enhance the robustness of the positioning function of the computer equipment.
Description
Technical Field
The embodiment of the invention relates to the technical field of automation control, in particular to a computer equipment positioning method and device, computer equipment and a storage medium.
Background
With the continuous development of science and technology, mobile computer equipment gradually appears in the aspect of life, for example, cleaning robot in the station, shopping guide robot in the market, food delivery robot in the dining room, etc., in order to realize different functions in different scenes, what should be solved at first is the location problem of computer equipment, and computer equipment needs to determine its position through the environment that is located, thereby according to the instruction that the user set in advance or assigned immediately realizes corresponding function.
In the prior art, computer equipment uses a camera and a laser radar to collect data and realizes positioning according to the collected data, however, the camera, the laser radar and other collection equipment are arranged in front of the computer equipment to collect the data of the environment in front of the computer equipment, and the data has limitation and can only reflect the current environment state at a certain moment. In reality, the environment changes with time, and when the environmental parameters change, the positioning accuracy of the computer equipment is easily reduced, so that the application scene of the computer equipment is restricted.
Disclosure of Invention
The invention provides a computer equipment positioning method, a computer equipment positioning device, computer equipment and a storage medium, which are used for realizing the positioning of computer equipment, reducing the influence of environmental change on positioning precision through a top view data set, improving the robustness of the positioning function of the computer equipment and reducing the occurrence probability of positioning failure.
In a first aspect, an embodiment of the present invention provides a method for positioning a computer device, where the method includes:
acquiring a top view data set, a head-up data set and a positioning data set of the computer equipment, wherein the top view data set comprises data of the inner top surface of a building where the computer equipment is located, and the head-up data set comprises data of a horizontal object in the building where the computer equipment is located;
determining a top view matching degree of the top view data set and a local map and a head-up matching degree of the head-up data set and the local map respectively based on the positioning data set;
determining a target pose in the local map based on the head-up matching degree and the head-up matching degree;
adding the top view data set and the head up data set to a local map based on the target pose and determining a location of the computer device on a global map.
In a second aspect, an embodiment of the present invention provides a computer positioning apparatus, including:
the data acquisition module is used for acquiring a top view data set, a head-up data set and a positioning data set of the computer equipment, wherein the top view data set comprises data of the inner top surface of a building where the computer equipment is located, and the head-up data set comprises data of a horizontal object in the building where the computer equipment is located;
a matching degree determination module for determining a top view matching degree of the top view data set and a local map and a head-up matching degree of the head-up data set and the local map respectively based on the positioning data set;
a local pose module to determine a target pose in the local map based on the head-up match metric and the head-up match metric;
a global positioning module to add the head-up dataset and the head-up dataset to a local map based on the target pose and to determine a location of the computer device on a global map.
In a third aspect, an embodiment of the present invention provides a computer device, where the computer device includes:
one or more processors;
a memory for storing one or more programs;
when executed by the one or more processors, cause the one or more processors to implement a computer device location method as in any of the embodiments of the present invention.
In a fourth aspect, the present invention provides a computer-readable storage medium, on which a computer program is stored, where the computer program is executed by a processor to implement the method for positioning a computer device according to any one of the embodiments of the present invention.
According to the embodiment of the invention, the data of the object in the building is collected in the horizontal direction of the computer equipment to form the head-up data set, the data of the top surface in the building is collected to be used as the top view data set, the positioning data set of the computer equipment can also be obtained, the top view matching degree of the top view data set and the local map and the head-up matching degree of the head-up data set and the local map are determined by taking the positioning data set as a standard, the target pose in the local map is determined based on the top view matching degree and the top view matching degree, and the position of the computer equipment is determined by the matching mode of the local map and the global map, so that the accurate positioning of the computer equipment is realized, the influence of environmental change on the positioning precision is reduced, the probability of positioning failure of the computer equipment is reduced, and the robustness of the.
Drawings
FIG. 1 is an exemplary diagram of computer device data collection in the prior art;
FIG. 2 is an exemplary diagram of a computer device data collection provided by an embodiment of the invention;
FIG. 3 is a flowchart of a method for locating a computer device according to an embodiment of the present invention;
FIG. 4 is a diagram illustrating an example of a computer device posture according to an embodiment of the present invention;
FIG. 5 is a diagram illustrating an exemplary arrangement of sensors provided in accordance with one embodiment of the present invention;
FIG. 6 is a flowchart of a method for positioning a computer device according to a second embodiment of the present invention;
FIG. 7 is a diagram illustrating an exemplary method for locating a computer device according to a second embodiment of the present invention;
FIG. 8 is a schematic structural diagram of a positioning apparatus of a computer device according to a third embodiment of the present invention;
fig. 9 is a schematic structural diagram of a computer device according to a fourth embodiment of the present invention.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the invention and are not limiting of the invention. It should be noted that, for convenience of description, only a part of the structures related to the present invention, not all of the structures, are shown in the drawings, and furthermore, embodiments of the present invention and features of the embodiments may be combined with each other without conflict.
Fig. 1 is an exemplary diagram of data collection of a computer device in the prior art, referring to fig. 1, a sensor 2 for collecting data in the prior art is often disposed in front of a computer device 1, and data of an object 3 placed in front of the computer device 1 is collected by the sensor 2, because in real life, the object 3 placed on the ground can change its position with time, the data collected by the computer device 1 on the object 3 can only have accuracy within a period of time, even when the object 3 is specifically a person, the position can change at any time, and the data collected by the computer device 1 cannot be used for positioning the computer device. Fig. 2 is an exemplary diagram of data acquisition of a computer device according to an embodiment of the present invention, and referring to fig. 2, the sensor 20 for acquiring data provided in the embodiment of the present invention is disposed at the top of the computer device 10, and in an actual environment, articles on the top of a ceiling and the like are often in a fixed state and do not change greatly in a short time.
Example one
Fig. 3 is a flowchart of a computer device positioning method according to an embodiment of the present invention, where the method is applicable to a situation of positioning a computer device in a scene with a large environmental change, and the method may be executed by a computer device positioning apparatus, where the apparatus may be implemented in a hardware and/or software manner, and referring to fig. 3, the computer device positioning method according to the embodiment of the present invention specifically includes the following steps:
The top view data set can include data of an inner top surface of a building where the computer equipment is located, the types of the data can include depth data, contour data, texture data and the like, and the inner top surface can be composed of objects on the top of the interior of the building and can include a ceiling of the building, an air conditioning opening, a lamp, an ornament and the like. The heads-up dataset may be data for objects to the side of the computer device and may include depth data, contour data, texture data, and the like. And the positioning data set can include the position information of the computer equipment, and can be obtained through devices such as wheeled encoder, base station location, satellite navigation and inertial navigation, and the data in the positioning data set can represent the position of the computer equipment at different moments, for example, the computer equipment receives the signaling sent by the base station through the 5G chip, and through the position information included in the signaling, the obtained position information can be added to the positioning data set.
Specifically, the computer device may be provided with a sensor such as a laser radar or a depth camera, and may acquire data of an inner ceiling surface and data of an object in a horizontal direction of a building in which the computer device is located, and may also acquire displacement data generated by the computer device in a moving process.
And 102, respectively determining the top view matching degree of the top view data set and the local map and the head-up matching degree of the head-up data set and the local map based on the positioning data set.
The local map can be data formed by one or more frames of pose data of the computer device, and can represent the position of the computer in a period of time. For example, a local map may reflect the location of the computer in the environment within 5 seconds from the point cloud data of the lidar within the first 5 seconds.
In the embodiment of the invention, the positioning data set can reflect the position change condition of the computer equipment in the moving process, and the positioning data set can be used as the basis for pose transformation of the computer equipment. Specifically, the position transformation condition of the computer equipment within a period of time can be determined through the positioning data set, and the top view data set and the head view data set are converted according to the position transformation condition, so that the top view data set and the head view data set are in the same coordinate system of the local map, and the matching degree can be conveniently determined.
Specifically, the data in the local map may include horizontal direction data and vertical direction data according to the acquisition direction, the data in the top view data set may be used to match with the vertical direction data in the local map to determine a corresponding matching degree, and the matching degree may be used as the top view matching degree, for example, the vertical direction data in the local map may be divided into a plurality of blocks, and the matching degree between the data in the top view data set and each block of vertical direction data may be determined respectively. The head-up match may be a match of the head-up data set with horizontal direction data in the local map. The matching mode can include direct matching, graph matching and the like, for example, a graph formed by data in the top view data set is matched with a map formed by data in the local map, and the matching degree of the graph is taken as the corresponding vertical matching degree.
The target pose may be a pose in which the computer device is converted from a current coordinate system to a coordinate system in which the local map is located, the pose may include a position and a posture of the computer device, the position may specifically be a three-dimensional coordinate (X, Y, Z) of the computer device in space, the posture may include a heading angle, a pitch angle, and a roll angle of the computer device, fig. 4 is an exemplary diagram of a posture of the computer device provided in an embodiment of the present invention, referring to fig. 4, a yaw angle of the computer device in an X-Z plane may be used as a pitch angle, a yaw angle in an X-Y plane may be used as a heading angle, and a yaw angle in a Y-Z plane may be used as a roll angle.
In the embodiment of the present invention, after determining the top view matching degree in the vertical direction and the head view matching degree in the horizontal direction of the computer device and the local map, the determined top view matching degree and the determined head view matching degree may be used as constraint conditions, and the target pose may be searched in the local map through the constraint conditions.
And 104, adding the top view data set and the head view data set to a local map based on the target pose, and determining the position of the computer equipment in a global map according to the local map.
The global map may include all data generated by the movement of the computer device in the space, may reflect the position and posture of the computer device in each place in the space, and may be generated by local map stitching.
In the embodiment of the invention, coordinate transformation can be carried out on the data in the top view data set and the head-up data set through the target pose, and the data in the top view data set and the head-up data set are added to the local map according to the coordinates after the coordinate transformation. For example, the local map may be divided into a plurality of grids according to the X-Y coordinates, and the Z-coordinates in the top view data set according to the X-Y coordinates of the data may be filled into the corresponding grids.
Specifically, after the local map is filled according to the top view data set and the head view data set, the local map can be matched with the global map, the position of the local map in the global map is determined, the position can be used as the position of the computer equipment in the space, and through the matching of the local map and the global map, the positioning characteristic of the computer equipment can be increased, and the positioning accuracy of the computer equipment is improved.
According to the embodiment of the invention, the top view data set, the head-up data set and the positioning data set of the computer equipment are obtained, the top view matching degree of the top view data set and the local map and the head-up matching degree of the head-up data set and the local map are determined based on the positioning data set, the target pose in the local map is determined through the top view matching degree and the horizontal matching degree, and the position of the computer equipment is determined in the global map after the local map is supplemented according to the target pose, so that the accurate positioning of the computer equipment is realized, the influence of the change of the surrounding environment into the positioning accuracy is reduced, and the robustness of the positioning function of the computer equipment can be improved.
Further, on the basis of the above embodiment of the present invention, the acquiring a top view data set, a head view data set and a positioning data set of a computer device includes: acquiring point cloud data of the inner top surface of a building where the computer equipment is located by at least two distance measuring sensors arranged at the top of the computer equipment and adding the point cloud data to a top view data set; acquiring point cloud data of an object in a building where the computer equipment is located by a ranging sensor arranged at the bottom of the computer equipment and adding the point cloud data to a head-up data set; and acquiring displacement data of the computer equipment by a wheel type encoder arranged on a main transmission shaft of the computer equipment, and adding the displacement data into a positioning data set.
The distance measuring sensor may be a sensor for measuring a distance between the computer device and the object, and may include at least one of a laser radar sensor, a depth camera, an infrared distance meter, and a time-out wave sensor. The point cloud data may be a set of point data that captures the surface of the object through a ranging sensor, which may represent the contour and depth of the object.
In the embodiment of the invention, a plurality of distance measuring sensors are arranged in the computer equipment, the distance measuring sensors can be sensors for measuring the distance between the computer equipment and an object and can comprise laser radar sensors, depth cameras, infrared distance meters and the like, the distance measuring sensors can be arranged at the top and the bottom of the computer equipment, point cloud data of the top surface in a building where the computer equipment is located can be obtained when the distance measuring sensors are arranged at the top, and point cloud data of the side surface of the computer equipment can be obtained when the distance measuring sensors are arranged at the bottom. The computer equipment can also be provided with a wheel type encoder, and the wheel type encoder can be arranged on a main transmission shaft of the computer equipment and can accurately acquire displacement data of the computer equipment. Fig. 5 is an exemplary diagram of a sensor arrangement according to an embodiment of the present invention, and referring to fig. 5, a distance measuring sensor 1 and a distance measuring sensor 2 may be disposed on a top of a computer device, the distance measuring sensor 1 and the distance measuring sensor 2 may collect data of a top surface of the computer in a building, a cross range may exist in a field of view of the distance measuring sensor 1 and the distance measuring sensor 2, and accuracy of data collection of the computer device on the top surface of the building may be improved by the distance measuring sensor 1 and the distance measuring sensor 2 disposed on the top. And the distance measuring sensor 3 of the computer device can be arranged at the bottom of the computer, and the distance measuring sensor 3 can collect the data of the object in the horizontal direction. The wheel type encoder is arranged on a driving wheel of the computer equipment, and displacement data such as the moving direction, the moving distance and the like of the computer equipment can be determined by measuring the rotating angle of the computer equipment during moving.
Further, on the basis of the above embodiment of the present invention, an included angle between a data collecting direction of a first ranging sensor of the two ranging sensors at the top of the computer device and a horizontal direction of the computer device is in a range of 30 ° to 90 °, an included angle between a data collecting direction of a second ranging sensor and the horizontal direction of the computer device is in a range of 10 ° to 20 °, and a height between the ranging sensor at the bottom of the computer device and the ground is in a range of 0.15 m to 0.3 m.
Further, on the basis of the above embodiment of the invention, the top view sensor is arranged on the top of the computer device.
Specifically, the top view sensor can be arranged at the top of the computer equipment, the top surface in the building corresponding to the top direction of the computer equipment can be collected, the shielding of the top view sensor by surrounding objects is prevented, the accuracy of data collection of the top surface in the building is improved, and the accuracy of positioning of the computer equipment is enhanced.
Further, on the basis of the above embodiment of the present invention, when the data collection field of view of the top view sensor is blocked by an obstacle, the direction of the top view sensor is changed so that the data collection field of view of the top view sensor is not blocked by the obstacle, wherein the obstacle is located between the computer device and the interior ceiling surface.
The data acquisition visual field can be the range of data acquisition of the top-view sensor, and the data acquisition visual field can be determined by the type and the setting position of the top-view sensor. The obstruction may be an object between the computer device and the ceiling of the building in which the computer device is located, which can prevent the ceiling sensors from collecting ceiling data.
In the embodiment of the invention, the movement device can be arranged between the top-view sensor and the computer equipment, and when the situation that the data acquisition visual field of the top-view sensor is blocked by the barrier is detected, the movement device can be controlled to change the installation angle, the installation position and the like of the top-view sensor, so that the influence of the barrier on the data acquisition visual field of the top-view sensor is reduced or avoided.
Example two
Fig. 6 is a flowchart of a computer device positioning method according to a second embodiment of the present invention, which is embodied on the basis of the second embodiment of the present invention, and determines a target pose in a local map by iteratively adjusting a pose determined by a head-up matching degree and a head-up matching degree, where referring to fig. 6, the method according to the second embodiment of the present invention includes the following steps:
The displacement data can comprise data such as the moving distance and the moving direction of the computer equipment, the displacement data in the positioning data set can be stored in association with the timestamp information, and the timestamp information corresponding to the displacement data can represent the acquisition time of the displacement.
Specifically, timestamp information corresponding to each displacement data may be extracted from the positioning data set.
And step 203, determining the pose change of the computer equipment in the corresponding time period based on the timestamp information.
The time periods can be a period of time for which the computer equipment moves, the starting time and the ending time of the time periods can be determined by the timestamp information respectively, different time periods can be determined by the combination of different timestamp information, and because the displacements corresponding to the timestamp information are different, the change degrees between the displacements corresponding to different time periods can be different, and the change degrees of the displacements in different time periods can be described by using the pose change.
In the embodiment of the invention, different time periods can be formed by different time stamp information, one time period can correspond to two time stamp information, and the change degree between the displacements corresponding to the two time stamp information is used as the pose change corresponding to the time period. Illustratively, the timestamp information is T0 and T1, and the displacement T1 and the displacement T2 in the range where the two pieces of timestamp information are stored in association are searched in the positioning data set according to T0 and T1, where the positioning data corresponding to the T0 timestamp time is T1, the positioning data corresponding to the T1 timestamp time is T2, and the pose change of the computer device from T0 to T1 may be T1T 2.
And 204, converting the top view data set according to the pose change, determining the top view matching degree with the local map, and converting the head-up data set according to the pose change, and determining the head-up matching degree with the local map.
Specifically, coordinate transformation may be performed on data in the top view data set and the head-up data set based on the pose change. The coordinate transformation may be followed by matching the top view dataset with the local map, for example, the top view dataset may be projected onto a planar grid coordinate system where the local map is located, each datum may be projected onto one grid in the planar grid coordinate system according to its horizontal and vertical coordinates, and the vertical coordinate of the datum may be stored as a height projection association onto the grid. And calculating the probability of matching the height projection of the data in the top view data set in the current grid with the height value of the data in the local map in the grid on Gaussian distribution aiming at each grid, and taking the sum of the probabilities corresponding to each data as the top view matching degree of the top view data set with the local map in the grid. The sum of the probabilities of the height values on the Gaussian distribution can be obtained
Determining, wherein Score represents matching rate, k tableNumber of point cloud data, h, of top view data set in gridiThe method comprises the steps of representing height projection of point cloud data subjected to top view data set after initial pose transformation conversion, representing Gaussian distribution parameters of height values of a local map by mu and sigma, representing the mean value of the height values of the point cloud data in the local map by mu, and representing the standard deviation of the height values of the point cloud data in the local map by sigma. The data in the head-up data set can be matched with the local map, the matching mode can comprise direct data matching, map matching and the like, and the obtained matching result can be used as the head-up matching degree.
Illustratively, the coordinate transforming the top view data set according to the pose change includes: the coordinates of the top view data set data are
Pose change
The process of coordinate transformation based on pose change comprises the following steps:
wherein, x and y can be respectively the abscissa and the ordinate of the data in the top view data set, represent the pose of the computer equipment, and theta can represent the turning angle of the computer equipment.
And step 205, determining the vertical pose corresponding to the data in the top view data set according to the top view matching degree.
The data in the top view data set represents data of an interior top surface of a building where the computer device is located, and may be position point information of an object on the interior top surface, and specifically may include contour information, depth information, texture information, and the like.
In the embodiment of the present invention, the top view matching degree may be generated by matching data in the top view data set with the local map, for example, when the data in the local map is matched with the data in the top view data set in batches, a plurality of top view matching degrees may be generated. The maximum value of all the top view matching degrees can be used as the target top view matching degree, the data of the local map corresponding to the target top view matching degree participating in matching can be determined, and the pose corresponding to the data can be used as the vertical pose corresponding to the data in the top view data set.
And step 206, determining the horizontal pose corresponding to the data in the head-up data set according to the head-up matching degree.
Specifically, a target head-up matching degree can be selected from all head-up matching degrees, and the selection mode can include random selection among head-up matching degrees larger than a threshold value, or a head-up matching degree with the largest value is selected as the target head-up matching degree, data in a local map corresponding to the target head-up matching degree is determined, and a position corresponding to the data can be used as a horizontal position corresponding to the data in the head-up data set.
And step 207, performing iterative adjustment on the vertical pose and the horizontal pose through a preset cost formula to serve as target poses in the local map.
The preset cost formula can be a formula for adjusting the vertical pose and the horizontal pose, when the value of the preset cost formula is minimum, the vertical pose and the horizontal pose are closest to the actual position of the computer equipment in the environment, and the preset cost formula can be expressed as follows:
In the embodiment of the invention, the acquired vertical pose and horizontal pose can be used as initial values of the preset cost formula, the pose in the preset cost formula is adjusted in an iterative manner, so that the value of the preset cost formula is minimum, and the pose in the preset cost formula when the value is minimum can be used as a target pose.
And 208, converting the top view data set and the head view data set according to the target pose and adding the converted top view data set and head view data set to the local map.
Specifically, the data in the top view data set and the head view data set are converted into a coordinate system of the local map through the target pose, and the data coordinates of the converted data are added to corresponding positions in the local map.
And 209, determining the map pose transformation relation between the local map and the global map.
The map pose transformation can be the corresponding relation of the coordinate systems of the local map and the global map.
In the embodiment of the invention, the data in the local map and the data in the global map can be matched to determine the highest matching degree of the local map data in the global map, and the pose corresponding to the highest matching degree in the global map can be determined as the map pose transformation of the local map and the global map.
And step 210, adjusting the local map based on the map pose transformation relation, and determining the position of the computer equipment on the global map according to the local map.
Specifically, the map pose transformation may transform data in the local map to a coordinate system corresponding to the global map, and a position of the local map in the global map may be used as a position of the computer device in the global map, for example, after the data in the local map is transformed according to the map pose, a position where the transformed local map data are similar or identical may be searched in the global map, and the position may be used as a position where the computer device is located in the space.
According to the embodiment of the invention, the topview matching degree of a vertical object in a local map and the head-up matching degree of a head-up data set in the local map are determined through the acquired topview data set, head-up data set and positioning data set, the corresponding vertical pose and horizontal pose are determined through the topview matching degree and the head-up matching degree, the target pose is determined after the vertical pose and the horizontal pose are iterated through a preset cost formula, the topview data set and the head-up data set are added into the local map according to the target pose, and the position of the computer equipment is determined through the matching of the local map and the global map, so that the accurate positioning of the computer equipment is realized, the influence of environmental change on the positioning function is reduced, the robustness of the positioning function of the computer equipment is enhanced, the application scene of the computer equipment can be expanded, and the experience degree of a.
Further, on the basis of the above embodiment of the present invention, the determining a head-up matching degree with a local map after transforming the head-up data set according to the pose change includes:
transforming coordinates of data in the head-up data set through the pose change; and performing scanning matching by using the converted data and map data in the local map, and taking a matching result of the scanning matching as the head-up matching degree.
The data in the head-up data set can be data of an object in the horizontal direction of a building where the computer equipment is located, the head-up data set can be obtained by collecting the object data through a depth camera, a laser radar sensor and an infrared distance measuring sensor, and the data in the head-up data set can comprise depth data, contour data, texture data and the like.
In an embodiment of the present invention, the data in the head-up data set may be composed of three-dimensional coordinates, and may represent the position of the object in space. Converting the coordinate of the data in the collected head-up data set into a coordinate system where the local Map is located through pose change, taking the head-up data set as a Scan matched with the Scan, taking the local Map as a Map matched with the Scan, and determining the matching degree of the Scan and the Map as the head-up matching degree through a preset rule of the Scan matching.
Fig. 7 is an exemplary diagram of a computer device positioning method according to a second embodiment of the present invention, referring to fig. 7, in which a wheel encoder is used to acquire a positioning data set, two lidar sensors disposed on the top of the computer device acquire a head-up data set, and a lidar sensor disposed on the bottom of the computer device acquires a head-up data set.
Step S1: a distance measuring sensor is arranged at the top of the robot, a wheel type encoder is arranged on a wheel shaft of a driving wheel of the robot, and laser data are collected in the moving process of the robot. When the distance measuring sensor is installed, the distance measuring sensor is required to be installed at the top of the robot, the included angle range between the data acquisition direction of the distance measuring sensor and the horizontal direction is [30 degrees ] and 50 degrees ], and when the included angle between the data acquisition direction of the distance measuring sensor and the horizontal direction is 90 degrees, the influence of surrounding objects on distance measuring sensing can be completely avoided. The timestamps of the data collected by the sensors may be aligned. When the two-dimensional laser sensor is adopted, the height range between the bottom of the robot and the ground is [0.15,0.3] m, and the two-dimensional laser sensor can be optimally arranged at the position where the bottom of the robot is 0.2m higher than the ground.
Step S2: and preprocessing the laser data acquired by the distance measuring sensor.
Further, step S2 may further include the following steps:
step S21: the data of the range finder 2 can be converted to the coordinate system of the range finder 1, the data of the range finder 1 and the data of the range finder 2 are commonly used as the data of the range finder, and the laser data is subjected to noise elimination using a statistical filter to eliminate the laser data whose average distance from the other laser data falls outside of [ -3 σ + μ, +3 σ + μ ], where σ and μ are the standard deviation and the mean of the average distance of the laser data, respectively.
Step S22: and removing the data falling on the vertical wall surface in the laser data, and only keeping the laser data falling on different planes of the ceiling. The step S22 is specifically implemented by the following steps:
and establishing a plane grid coordinate system by taking the distance measuring sensor as an origin, converting the laser data into a Cartesian coordinate system, installing the laser data into an X-Y projection to the established plane grid coordinate system, and recording the coordinates of the laser data in each grid. And traversing each grid, recording the maximum value and the minimum value of the vertical coordinate Z value of the laser data in each grid, and when the difference between the maximum value and the minimum value in the grid is greater than a threshold value, the laser data in the grid is a laser spot falling on a vertical wall surface, and discarding the laser data corresponding to the grid. The threshold value may typically take on the order of 0.5.
Step S3: and calculating pose transformation between the acquired range finder data and a preset top-view local map. And the pose of the two-dimensional laser data is transformed with the pose of the two-dimensional laser local map.
Wherein, step S3 may further include the following steps:
step S31: and calculating the time difference between the laser data acquired by the ranging sensor at the current moment and the last frame of laser data acquired by the ranging sensor by using the laser data and the encoder data after the timestamp is aligned, and acquiring the pose change of the robot motion in the time difference from the encoder by using the time difference.
For example, the encoder data obtained from the encoder is denoted as (t)0,T0),(t1,T1),....,(tn,Tn) Where t represents a time stamp when the encoder data was acquired. T denotes encoder data acquired at time T, and subscripts 0, 1, and n denote the order of acquisition of the encoder data. If the start time of the time difference between the distance measuring sensors is k-1 and the end time of the time difference between the distance measuring sensors is k, the pose is transformed to Tk-1*Tk。
Step S32: using the pose transformation obtained in step S31 as the initial of matching between the laser data and the local map, projecting the laser data to a planar grid coordinate system where the local map is located in an X-Y plane, determining a grid corresponding to each laser data, matching the vertical coordinate Z value of the laser data with the data of the local map in the grid, where the vertical coordinate Z value in the same grid obeys gaussian distribution, calculating the matching rate between the laser data and the local map according to the Z value, and using the pose corresponding to the laser data with the highest matching rate as the pose transformation between the laser data and the local map, where the calculation formula of the matching rate may be as follows:
Step S33, after the pose transformation is determined in step S32, the pose transformation may be further optimized non-linearly by a cost formula, which may be as follows:
wherein e represents cost, n represents the number of point cloud data in the top view data set, mu and sigma represent Gaussian distribution parameters with which the height values of the local map meet, and hiThe height projection of the point cloud data in the top view data set after the initial pose transformation is represented, T' represents the pose transformation determined by the encoder data in step S3, T represents the pose transformation, and ω represents the error influence of the auxiliary positioning sensor. And (3) the cost formula is iteratively calculated by adjusting the value of the pose transformation T, so that the result of the cost e is minimum, and when the result of the cost e is minimum, the corresponding pose transformation T can be a result value subjected to nonlinear optimization.
Step S34: and matching the two-dimensional laser data with the two-dimensional laser local map through Scan Match (scanning matching), and establishing a matching result between the two-dimensional laser data and the two-dimensional laser local map. The matching result can be optimized by a preset cost formula, which can be as follows:
Step S4: the range finder data and the two-dimensional laser data are added to the local map using the pose transform obtained in step S3.
Further, step S4 specifically includes the following steps: and S3, obtaining the pose, converting the laser data into a coordinate system corresponding to the local map, determining the projection on an X-Y plane by using the coordinate parameters of the laser data under the local map coordinate, determining the grids corresponding to the laser data, and updating the height distribution in the corresponding grids by using the Z value in the coordinate parameters.
Step S5: and matching the local map added with the laser data with a pre-generated global map to determine pose transformation between the local map and the global map.
Further, step S5 specifically includes the following steps: projecting the coordinates of all laser data in the local map according to an X-Y plane, determining a corresponding grid of each laser data in the X-Y plane, matching the Z value in the coordinates with the Z value of the grid in the global map, and determining a corresponding matching rate, wherein a calculation formula of the matching rate is as follows:
where Score denotes the matching rate, k denotes the number of local map laser data falling within the grid, hiThe method comprises the steps of representing the height value of laser data after initial pose transformation conversion, representing Gaussian distribution parameters which the height value of a global map conforms to by mu and sigma, representing the mean value of the height value of data in the global map by mu, and representing the standard deviation of the height value of the data in the global map by sigma.
Step S6: and determining whether the robot is in a mapping mode, if so, continuing to execute the step S7, and if not, taking the position with the highest matching rate in the global map as the robot positioning determination position.
Step S7: and adding the laser data in the local map to the global map according to the pose corresponding to the highest matching rate in the step S5.
On the basis of the embodiment of the invention, the position and posture from the local map to the global map are determined as TG LThe superscript G denotes a global map coordinate system, the subscript L denotes a local map coordinate system, and any one of the laser data in the local map may be denoted as pL iWhere the superscript L denotes a local coordinate system and the subscript i denotes a serial number of each point in the local map, the process of converting point cloud data of the local map to the global map can be expressed as
The transformed point cloud data can be determined into grid coordinates in the global map to determine grids in a planar grid coordinate system of the global map, and the height distribution of the corresponding grids is updated according to the height projection of the point cloud data.
EXAMPLE III
Fig. 8 is a schematic structural diagram of a positioning apparatus for computer equipment according to a third embodiment of the present invention, and the apparatus shown in fig. 8 can execute the positioning method for computer equipment according to any embodiment of the present invention, and has corresponding functional modules and beneficial effects of the execution method. The device can be implemented by software and/or hardware, and specifically comprises: a data acquisition module 301, a matching degree determination module 302, a local pose module 303 and a global positioning module 304.
The data acquisition module 301 is configured to acquire a top view data set, a head-up data set, and a positioning data set of the computer device, where the top view data set includes data of an inner top surface of a building where the computer device is located, and the head-up data set includes data of a horizontal object in the building where the computer device is located.
A matching degree determination module 302, configured to determine a top view matching degree of the top view data set and a local map and a head-up matching degree of the head-up data set and the local map, respectively, based on the positioning data set.
A local pose module 303 to determine a target pose in the local map based on the head-up match and the head-up match.
And the global positioning module 304 is used for determining the position of the computer equipment on the basis of the local map after the target pose is added and according to the local map on the global map.
According to the embodiment of the invention, the top view data set, the head-up data set and the positioning data set of the computer equipment are acquired through the data acquisition module, the matching degree determination module determines the top view matching degree of the top view data set and the local map and the head-up matching degree of the head-up data set and the local map based on the positioning data set, the local pose module determines the target pose in the local map through the top view matching degree and the horizontal matching degree, and the global positioning module determines the position of the computer equipment in the global map after supplementing the local map according to the target pose, so that the accurate positioning of the computer equipment is realized, the influence of the change of the surrounding environment into the positioning precision is reduced, and the robustness of the positioning function of the computer equipment can be improved.
Further, on the basis of the above embodiment of the invention, the data acquisition module 301 includes:
and the vertical acquisition unit is used for acquiring point cloud data of the inner top surface of the building where the computer equipment is positioned by at least two distance measurement sensors arranged at the top of the computer equipment and adding the point cloud data into the top view data set.
And the horizontal acquisition unit is used for acquiring point cloud data of an object in a building where the computer equipment is positioned through a distance measurement sensor arranged at the bottom of the computer equipment and adding the point cloud data to the head-up data set.
And the displacement acquisition unit is used for acquiring displacement data of the computer equipment and adding the displacement data into the positioning data set through a wheel type encoder arranged on a main transmission shaft of the computer equipment.
Further, on the basis of the above embodiment of the present invention, the matching degree determining module 302 includes:
and the time determining unit is used for determining the timestamp information corresponding to the displacement data in the positioning data set.
And the pose change unit is used for determining the pose change of the computer equipment in the corresponding time period based on the timestamp information.
And the matching degree unit is used for converting the top view data set according to the pose change and then determining the top view matching degree with a local map, and converting the head-up data set according to the pose change and then determining the head-up matching degree with the local map.
Further, on the basis of the above embodiment of the present invention, the matching degree unit includes:
and the coordinate conversion subunit is used for converting the coordinates of the data in the head-up data set through the pose change.
And the scanning matching subunit is used for performing scanning matching by using the converted data and the map data in the local map, and taking the matching result of the scanning matching as the head-up matching degree.
Further, on the basis of the above embodiment of the present invention, the local pose module 303 includes:
and the vertical pose unit is used for determining the vertical pose corresponding to the data in the top view data set according to the top view matching degree.
And the horizontal pose unit is used for determining the horizontal pose corresponding to the data in the head-up data set according to the head-up matching degree.
And the pose optimization unit is used for performing iterative adjustment on the vertical pose and the horizontal pose through a preset cost formula to be used as the target pose in the local map.
Further, on the basis of the above embodiment of the present invention, the global positioning module 304 includes:
and the local updating unit is used for converting the top view data set and the head view data set according to the target pose and then adding the converted top view data set and the converted head view data set to a local map.
And the map transformation unit is used for determining the map pose transformation relation between the local map and the global map.
And the position determining unit is used for determining the position of the computer equipment on the global map according to the local map after the local map is adjusted based on the map pose transformation relation.
Further, on the basis of the above embodiment of the present invention, an included angle between a data collecting direction of a first ranging sensor of the two ranging sensors at the top of the computer device and a horizontal direction of the computer device is in a range of 30 ° to 90 °, an included angle between a data collecting direction of a second ranging sensor and the horizontal direction of the computer device is in a range of 10 ° to 20 °, and a height between the ranging sensor at the bottom of the computer device and the ground is in a range of 0.15 m to 0.3 m.
Further, on the basis of the above embodiment of the invention, the top view sensor in the device is arranged on the top of the computer equipment.
Further, on the basis of the embodiment of the present invention, the system further includes an acquisition adjusting module, configured to change a direction of the top view sensor when a data acquisition field of view of the top view sensor is blocked by an obstacle, so that the data acquisition field of view of the top view sensor is not blocked by the obstacle, where the obstacle is located between the computer device and the inner top surface.
Example four
Fig. 9 is a schematic structural diagram of a computer apparatus according to a fourth embodiment of the present invention, as shown in fig. 9, the computer apparatus includes a processor 40, a memory 41, an input device 42, and an output device 43; let the number of processors 40 in the computer device be one or more, and one processor 40 is taken as an example in fig. 9; the processor 40, the memory 41, the input device 42 and the output device 43 in the computer apparatus may be connected by a bus or other means, and the connection by the bus is exemplified in fig. 9.
The memory 41 serves as a computer-readable storage medium for storing software programs, computer-executable programs, and modules, such as program instructions/modules corresponding to the computer device locating method in the embodiment of the present invention (for example, the data acquisition module 301, the matching degree determination module 302, the local pose module 303, and the global positioning module 304 in the computer device locating apparatus). The processor 40 executes various functional applications and data processing of the computer device by executing software programs, instructions and modules stored in the memory 41, that is, implements the computer device method described above.
The memory 41 may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function; the storage data area may store data created according to the use of the terminal, and the like. Further, the memory 41 may include high speed random access memory, and may also include non-volatile memory, such as at least one magnetic disk storage device, flash memory device, or other non-volatile solid state storage device. In some examples, memory 41 may further include memory located remotely from processor 40, which may be connected to a computer device over a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The input device 42 is operable to receive input numeric or character information and to generate key signal inputs relating to user settings and function controls of the computer apparatus. The output device 43 may include a display device such as a display screen.
EXAMPLE five
An embodiment of the present invention further provides a storage medium containing computer-executable instructions, which when executed by a computer processor, perform a method for locating a computer device, the method including:
acquiring a top view data set, a head-up data set and a positioning data set of the computer equipment, wherein the top view data set comprises data of the inner top surface of a building where the computer equipment is located, and the head-up data set comprises data of a horizontal object in the building where the computer equipment is located;
determining a top view matching degree of the top view data set and a local map and a head-up matching degree of the head-up data set and the local map respectively based on the positioning data set;
determining a target pose in the local map based on the head-up matching degree and the head-up matching degree;
adding the top view data set and the heads up data set to a local map based on the target pose and determining a location of the computer device in a global map from the local map.
Of course, the storage medium containing the computer-executable instructions provided by the embodiments of the present invention is not limited to the method operations described above, and may also perform related operations in the positioning of the computer device provided by any embodiments of the present invention.
From the above description of the embodiments, it is obvious for those skilled in the art that the present invention can be implemented by software and necessary general hardware, and certainly, can also be implemented by hardware, but the former is a better embodiment in many cases. Based on such understanding, the technical solutions of the present invention may be embodied in the form of a software product, which may be stored in a computer-readable storage medium, such as a floppy disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a FLASH Memory (FLASH), a hard disk or an optical disk of a computer, and includes several instructions for enabling a computer device (which may be a personal computer, a server, or a network device) to execute the methods according to the embodiments of the present invention.
It should be noted that, in the embodiment of the positioning apparatus for computer equipment, the units and modules included in the positioning apparatus for computer equipment are only divided according to functional logic, but are not limited to the above division as long as the corresponding functions can be implemented; in addition, specific names of the functional units are only for convenience of distinguishing from each other, and are not used for limiting the protection scope of the present invention.
It is to be noted that the foregoing is only illustrative of the preferred embodiments of the present invention and the technical principles employed. It will be understood by those skilled in the art that the present invention is not limited to the particular embodiments described herein, but is capable of various obvious changes, rearrangements and substitutions as will now become apparent to those skilled in the art without departing from the scope of the invention. Therefore, although the present invention has been described in greater detail by the above embodiments, the present invention is not limited to the above embodiments, and may include other equivalent embodiments without departing from the spirit of the present invention, and the scope of the present invention is determined by the scope of the appended claims.
Claims (12)
1. A computer device location method, the method comprising:
acquiring a top view data set, a head-up data set and a positioning data set of the computer equipment, wherein the top view data set comprises data of the inner top surface of a building where the computer equipment is located, and the head-up data set comprises data of a horizontal object in the building where the computer equipment is located;
determining a top view matching degree of the top view data set and a local map and a head-up matching degree of the head-up data set and the local map respectively based on the positioning data set;
determining a target pose in the local map based on the head-up matching degree and the head-up matching degree;
adding the top view data set and the heads up data set to a local map based on the target pose and determining a location of the computer device in a global map from the local map.
2. The method of claim 1, wherein acquiring a top view dataset, a head up dataset, and a positioning dataset of a computer device comprises:
acquiring point cloud data of the inner top surface of a building where the computer equipment is located by at least two distance measuring sensors arranged at the top of the computer equipment and adding the point cloud data to a top view data set;
acquiring point cloud data of an object in a building where the computer equipment is located by a ranging sensor arranged at the bottom of the computer equipment and adding the point cloud data to a head-up data set;
and acquiring displacement data of the computer equipment by a wheel type encoder arranged on a main transmission shaft of the computer equipment, and adding the displacement data into a positioning data set.
3. The method of claim 1, wherein the determining a top view match of the top view dataset to a local map and a head up match of the head up dataset to a local map based on the positioning dataset comprises:
determining timestamp information corresponding to displacement data in a positioning data set;
determining pose changes of the computer equipment in corresponding time periods based on the timestamp information;
and converting the top view data set according to the pose change to determine the top view matching degree with a local map, and converting the head view data set according to the pose change to determine the head view matching degree with the local map.
4. The method as claimed in claim 3, wherein the determining a head-up matching with a local map after transforming the head-up data set according to the pose change comprises:
transforming coordinates of data in the head-up data set through the pose change;
and performing scanning matching by using the converted data and map data in the local map, and taking a matching result of the scanning matching as the head-up matching degree.
5. The method as recited in claim 3, wherein the determining a target pose in the local map based on the head-up match metric and the head-up match metric comprises:
determining a vertical pose corresponding to data in the top view data set according to the top view matching degree;
determining a horizontal pose corresponding to data in a head-up data set according to the head-up matching degree;
and performing iterative adjustment on the vertical pose and the horizontal pose through a preset cost formula to be used as the target pose in the local map.
6. The method of claim 5, wherein the adding the top view data set and the heads up data set to a local map based on the target pose and determining the location of the computer device in a global map from the local map comprises:
converting the top view data set and the head view data set according to the target pose and then adding the converted top view data set and the converted head view data set to a local map;
determining a map pose transformation relation between the local map and the global map;
and after the local map is adjusted based on the map pose transformation relation, determining the position of the computer equipment on the global map according to the local map.
7. The method of claim 2, wherein the data collecting direction of the first ranging sensor of the two ranging sensors at the top of the computer device is within 30 ° to 90 ° from the horizontal direction of the computer device, the data collecting direction of the second ranging sensor is within 10 ° to 20 ° from the horizontal direction of the computer device, and the height of the ranging sensor at the bottom of the computer device from the ground is within 0.15 m to 0.3 m.
8. The method of claim 1, wherein the head-view sensor is disposed on top of the computer device.
9. The method of claim 1, wherein the data acquisition field of view of the overhead view sensor is obstructed by an obstacle positioned between the computer device and the interior ceiling surface, and wherein the orientation of the overhead view sensor is changed such that the data acquisition field of view of the overhead view sensor is unobstructed by the obstacle.
10. An apparatus for locating a computer device, the apparatus comprising:
the data acquisition module is used for acquiring a top view data set, a head-up data set and a positioning data set of the computer equipment, wherein the top view data set comprises data of the inner top surface of a building where the computer equipment is located, and the head-up data set comprises data of a horizontal object in the building where the computer equipment is located;
a matching degree determination module for determining a top view matching degree of the top view data set and a local map and a head-up matching degree of the head-up data set and the local map respectively based on the positioning data set;
a local pose module to determine a target pose in the local map based on the head-up match metric and the head-up match metric;
a global positioning module to add the head-up data set and the head-up data set to a local map based on the target pose and to determine a location of the computer device in a global map according to the local map.
11. A computer device, characterized in that the computer device comprises:
one or more processors;
a memory for storing one or more programs;
when executed by the one or more processors, cause the one or more processors to implement the computer device location method of any of claims 1-9.
12. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the computer device positioning method according to any one of claims 1-9.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010744438.6A CN111862214B (en) | 2020-07-29 | 2020-07-29 | Computer equipment positioning method, device, computer equipment and storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010744438.6A CN111862214B (en) | 2020-07-29 | 2020-07-29 | Computer equipment positioning method, device, computer equipment and storage medium |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN111862214A true CN111862214A (en) | 2020-10-30 |
| CN111862214B CN111862214B (en) | 2023-08-25 |
Family
ID=72945945
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010744438.6A Active CN111862214B (en) | 2020-07-29 | 2020-07-29 | Computer equipment positioning method, device, computer equipment and storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111862214B (en) |
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113325433A (en) * | 2021-05-28 | 2021-08-31 | 上海高仙自动化科技发展有限公司 | Positioning method, positioning device, electronic equipment and storage medium |
| CN113324551A (en) * | 2021-05-28 | 2021-08-31 | 上海高仙自动化科技发展有限公司 | Positioning method, positioning device, electronic equipment and storage medium |
| CN113340293A (en) * | 2021-05-28 | 2021-09-03 | 上海高仙自动化科技发展有限公司 | Positioning method, positioning device, electronic equipment and storage medium |
| CN113450597A (en) * | 2021-06-09 | 2021-09-28 | 浙江兆晟科技股份有限公司 | Ship auxiliary navigation method and system based on deep learning |
| WO2022246812A1 (en) * | 2021-05-28 | 2022-12-01 | 上海高仙自动化科技发展有限公司 | Positioning method and apparatus, electronic device, and storage medium |
Citations (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20140350839A1 (en) * | 2013-05-23 | 2014-11-27 | Irobot Corporation | Simultaneous Localization And Mapping For A Mobile Robot |
| CN106123890A (en) * | 2016-06-14 | 2016-11-16 | 中国科学院合肥物质科学研究院 | A kind of robot localization method of Fusion |
| CN106338287A (en) * | 2016-08-24 | 2017-01-18 | 杭州国辰牵星科技有限公司 | Ceiling-based indoor moving robot vision positioning method |
| CN107689063A (en) * | 2017-07-27 | 2018-02-13 | 南京理工大学北方研究院 | A kind of robot indoor orientation method based on ceiling image |
| CN108717710A (en) * | 2018-05-18 | 2018-10-30 | 京东方科技集团股份有限公司 | Localization method, apparatus and system under indoor environment |
| CN108732584A (en) * | 2017-04-17 | 2018-11-02 | 百度在线网络技术（北京）有限公司 | Method and apparatus for updating map |
| CN109671120A (en) * | 2018-11-08 | 2019-04-23 | 南京华捷艾米软件科技有限公司 | A kind of monocular SLAM initial method and system based on wheel type encoder |
| CN109813310A (en) * | 2019-03-11 | 2019-05-28 | 中南大学 | Underground working apparatus localization method, device, system and storage medium |
| WO2019140745A1 (en) * | 2018-01-16 | 2019-07-25 | 广东省智能制造研究所 | Robot positioning method and device |
| CN110189399A (en) * | 2019-04-26 | 2019-08-30 | 浙江大学 | A kind of method and system that interior three-dimensional layout rebuilds |
| CN110686677A (en) * | 2019-10-10 | 2020-01-14 | 东北大学 | Global positioning method based on geometric information |
| CN111089585A (en) * | 2019-12-30 | 2020-05-01 | 哈尔滨理工大学 | Mapping and positioning method based on sensor information fusion |
-
2020
- 2020-07-29 CN CN202010744438.6A patent/CN111862214B/en active Active
Patent Citations (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20140350839A1 (en) * | 2013-05-23 | 2014-11-27 | Irobot Corporation | Simultaneous Localization And Mapping For A Mobile Robot |
| CN106123890A (en) * | 2016-06-14 | 2016-11-16 | 中国科学院合肥物质科学研究院 | A kind of robot localization method of Fusion |
| CN106338287A (en) * | 2016-08-24 | 2017-01-18 | 杭州国辰牵星科技有限公司 | Ceiling-based indoor moving robot vision positioning method |
| CN108732584A (en) * | 2017-04-17 | 2018-11-02 | 百度在线网络技术（北京）有限公司 | Method and apparatus for updating map |
| CN107689063A (en) * | 2017-07-27 | 2018-02-13 | 南京理工大学北方研究院 | A kind of robot indoor orientation method based on ceiling image |
| WO2019140745A1 (en) * | 2018-01-16 | 2019-07-25 | 广东省智能制造研究所 | Robot positioning method and device |
| CN108717710A (en) * | 2018-05-18 | 2018-10-30 | 京东方科技集团股份有限公司 | Localization method, apparatus and system under indoor environment |
| CN109671120A (en) * | 2018-11-08 | 2019-04-23 | 南京华捷艾米软件科技有限公司 | A kind of monocular SLAM initial method and system based on wheel type encoder |
| CN109813310A (en) * | 2019-03-11 | 2019-05-28 | 中南大学 | Underground working apparatus localization method, device, system and storage medium |
| CN110189399A (en) * | 2019-04-26 | 2019-08-30 | 浙江大学 | A kind of method and system that interior three-dimensional layout rebuilds |
| CN110686677A (en) * | 2019-10-10 | 2020-01-14 | 东北大学 | Global positioning method based on geometric information |
| CN111089585A (en) * | 2019-12-30 | 2020-05-01 | 哈尔滨理工大学 | Mapping and positioning method based on sensor information fusion |
Non-Patent Citations (2)
| Title |
|---|
| YANG YU 等: "Research on Accurate Positioning of Indoor Objects Based on ROS and 3D Point Cloud", 《JOURNAL OF PHYSICS》 * |
| 王晓彤 等: "基于单目天花板视觉的扫地机器人定位算法设计及实现", 《微电子学与计算机》 * |
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113325433A (en) * | 2021-05-28 | 2021-08-31 | 上海高仙自动化科技发展有限公司 | Positioning method, positioning device, electronic equipment and storage medium |
| CN113324551A (en) * | 2021-05-28 | 2021-08-31 | 上海高仙自动化科技发展有限公司 | Positioning method, positioning device, electronic equipment and storage medium |
| CN113340293A (en) * | 2021-05-28 | 2021-09-03 | 上海高仙自动化科技发展有限公司 | Positioning method, positioning device, electronic equipment and storage medium |
| WO2022246812A1 (en) * | 2021-05-28 | 2022-12-01 | 上海高仙自动化科技发展有限公司 | Positioning method and apparatus, electronic device, and storage medium |
| CN113450597A (en) * | 2021-06-09 | 2021-09-28 | 浙江兆晟科技股份有限公司 | Ship auxiliary navigation method and system based on deep learning |
| CN113450597B (en) * | 2021-06-09 | 2022-11-29 | 浙江兆晟科技股份有限公司 | Ship auxiliary navigation method and system based on deep learning |
Also Published As
| Publication number | Publication date |
|---|---|
| CN111862214B (en) | 2023-08-25 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN112347840B (en) | Vision sensor laser radar integrated unmanned aerial vehicle positioning and image building device and method | |
| CN112567201B (en) | Distance measuring method and device | |
| CN111862214A (en) | Computer equipment positioning method and device, computer equipment and storage medium | |
| CN111429574B (en) | Mobile robot positioning method and system based on three-dimensional point cloud and vision fusion | |
| CN111862219B (en) | Computer equipment positioning method and device, computer equipment and storage medium | |
| WO2018009263A1 (en) | Systems and methods for mapping an environment | |
| AU2015210742A1 (en) | Augmented three dimensional point collection of vertical structures | |
| CN114419147A (en) | Rescue robot intelligent remote human-computer interaction control method and system | |
| CN111862215B (en) | Computer equipment positioning method and device, computer equipment and storage medium | |
| CN112179357B (en) | Monocular camera-based visual navigation method and system for plane moving target | |
| CN110736456A (en) | Two-dimensional laser real-time positioning method based on feature extraction in sparse environment | |
| CN113313765B (en) | Positioning method, positioning device, electronic equipment and storage medium | |
| CN111862216A (en) | Computer equipment positioning method and device, computer equipment and storage medium | |
| CN115639547A (en) | Multi-line laser radar and GNSS-INS combined calibration method, system and medium | |
| WO2022021133A1 (en) | Computer device positioning method and apparatus, and computer device and storage medium | |
| WO2022246812A1 (en) | Positioning method and apparatus, electronic device, and storage medium | |
| CN114529585A (en) | Mobile equipment autonomous positioning method based on depth vision and inertial measurement | |
| WO2022021132A1 (en) | Computer device positioning method and apparatus, computer device, and storage medium | |
| KR101502071B1 (en) | Camera Data Generator for Landmark-based Vision Navigation System and Computer-readable Media Recording Program for Executing the Same | |
| CN116380039A (en) | Mobile robot navigation system based on solid-state laser radar and point cloud map | |
| US20110280473A1 (en) | Rotation estimation device, rotation estimation method, and record medium | |
| CN115773759A (en) | Indoor positioning method, device and equipment of autonomous mobile robot and storage medium | |
| CN115060268A (en) | Fusion positioning method, system, equipment and storage medium for machine room | |
| CN113313764B (en) | Positioning method, positioning device, electronic equipment and storage medium | |
| CN113029166B (en) | Positioning method, positioning device, electronic equipment and storage medium |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |