CN113340296A - Method and device for automatically updating mobile robot map - Google Patents
Method and device for automatically updating mobile robot map Download PDFInfo
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- G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
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- G01C21/3804—Creation or updating of map data
- G01C21/3807—Creation or updating of map data characterised by the type of data
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- G—PHYSICS
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- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
- G01C21/38—Electronic maps specially adapted for navigation; Updating thereof
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Abstract
The invention relates to a method and a device for automatically updating a mobile robot map, wherein the method comprises the following steps: s1, setting an anchor point at a global coordinate position of a preset map, setting a first identification characteristic for the anchor point, constructing the map by adopting laser point cloud, and screening the anchor point through a preset first characteristic threshold; s2, obtaining the global coordinate value of the map where the anchor point is located according to a first algorithm; s3, when the mobile robot detects that the current positioning position is lower than a preset threshold value, starting a mapping mode, and acquiring the pose of the mobile robot according to a second algorithm; s4, constructing a local map, establishing a map updating block by using the anchor point as a constraint, and replacing the corresponding block of the map to finish updating. Therefore, the navigation map can be accurately and automatically updated when the local environment is changed violently.
Description
Technical Field
The invention relates to the technical field of laser navigation mobile robot algorithms, in particular to a method and a device for automatically updating a laser point cloud map formed based on a reflective column as a reference.
Background
At present, the intelligent degree of the field of factory and warehouse logistics is higher and higher, the mode of constructing an environment map by utilizing a laser SLAM technology and then conducting mobile robot navigation is more and more popular, and due to the fact that the implementation cost is low, deployment is simple and rapid, and more attention is paid.
However, in part of factory environments, since objects in the environment often move and change, the actual environment and a laser profile map scanned in advance have large deviation, so that deviation occurs when the laser profile map is referred to in the actual operation process of the mobile robot, the operation route and the position of the mobile robot are inconsistent with the expectation, and the all-weather automatic operation cannot be satisfied.
At present, the common method is to change the laser installation position of the mobile robot to ensure that a reference object on a two-dimensional laser scanning plane is basically stable and does not change, or to frequently participate in map rescanning and splicing manually, but the methods are difficult to completely avoid the interference of a dynamic environment and really realize automatic updating.
For this purpose, the prior art proposes a static map online updating method and apparatus (CN 110164288A) based on self-constructed map, wherein the method steps include: a. acquiring an initial scanning map; b. mapping the initial scanning map; c. acquiring the actual position information of the movable robot, blank space information and detectable object information in a scanning range, updating an initial scanning map, and acquiring a numerical scanning map; d. comparing the digitized scanning map with the existing map, updating the existing map, judging blank space information and detectable object information, and determining a next-step moving route; e. and repeating the steps until the movable robot finishes updating the static map.
However, the above prior art has the disadvantages that the existing map updating method can only be applied to a part with a very small change of the local environment, and therefore, the existing map updating method can only be operated in a working environment with a gradually changed local environment, and meanwhile, the existing map updating method must rely on the premise that a large part of reference objects in the existing map cannot be changed, so that the existing map updating method is out of reality to a certain extent, and the map updating effect and accuracy under the severe local environment are difficult to ensure.
Disclosure of Invention
The invention mainly aims to provide a method and a device for automatically updating a mobile robot map so as to realize accurate and automatic updating of a navigation map when a local environment is changed violently.
In order to achieve the above object, according to an aspect of the present invention, there is provided a method of automatically updating a map of a mobile robot, the steps including:
s1, setting an anchor point at a global coordinate position of a preset map, setting a first identification characteristic for the anchor point, constructing the map by adopting laser point cloud, and screening the anchor point through a preset first characteristic threshold;
s2, obtaining the global coordinate value of the map where the anchor point is located according to a first algorithm;
s3, when the mobile robot detects that the current positioning position is lower than a preset threshold value, starting a mapping mode, and acquiring the pose of the mobile robot according to a second algorithm;
s4, constructing a local map, establishing a map updating block by using the anchor point as a constraint, and replacing the corresponding block of the map to finish updating.
In a possible preferred embodiment, the step of constructing a map by using the laser point cloud in step S1 includes:
s11 records the current frame t0Laser pose T0After the laser point data is converted into the current coordinate system, a kdTere is established for the laser point data, and the laser pose T of the next moment ti is estimated by using the uniform motion model V and the laser time interval delta TiWherein T isi＝T0+ V Δ t, and in addition V is updated according to the calculated pose change;
s12 the laser point data at the next moment is processed according to the pose TiMake a conversion for tiEach laser spot (x) at a timei，yi) Searching for the coordinates (Lx) of the current closest point using kdTerei，Lyi) To establish an objective function according to the pose of the current time
S13 iterative optimization of objective function to obtain optimal current pose TiObtaining a minimum value to satisfy the objective function;
s14 sets a key frame according to the motion distance and time, sets the laser frame meeting the conditions as the key frame, simultaneously carries out closed-loop detection, and completes the whole SLAM mapping by using a mapping optimization algorithm.
In a possible preferred embodiment, the anchor point is a reflective pillar, the first identification feature of the anchor point is a reflective characteristic, and the first feature threshold includes: a reflectivity threshold distinguished from the environment object, and at least one of a reflective column radius threshold.
In a possible preferred embodiment, the first algorithm step in step S2 includes:
s21, acquiring the distance, angle data and reflectivity of the current mobile robot motion position moment, and extracting the anchor point laser point coordinates of the current moment through a reflectivity threshold; the coordinates of the anchor point laser points are expressed under the current laser local coordinate system;
s22 for each laser point PiAnd (i is 1, 2, 3 … n), calculating the Euclidean distance between adjacent laser points, extracting laser points belonging to the anchor points through a clustering algorithm when the distance is smaller than a preset threshold and the reflectivity reaches a first characteristic threshold, and fitting the coordinates of the laser points in the global map through the shape of the anchor points to obtain the global coordinates of the map where the center positions of the anchor points are located.
In a possible preferred embodiment, the second algorithm step in step S3 includes:
s31 obtaining the current time tiMoving robot pose TiConverting the laser point data according to the current time position and posture to obtain the global laser point coordinate (x)i，yi)；
S32 extracting the coordinates (reflector _ x) of the laser point belonging to the anchor point according to the first characteristic thresholdi，reflector_yi) (ii) a Establishing kdtree by using the existing map coordinate points; respectively searching for the corresponding closest laser spot (map _ x)i，map_yi) And the anchor coordinates in the map (map _ reflector _ x)i，map_reflector_yi) Calculating an optimized objective function f (T)i)＝[(xi-map_xi)2+(yi-map_yi)2+pWeight*((reflector_xi-map_reflector_xi)2+(reflector_yi-map_reflector_yi)2)]；
And S32, carrying out iterative solution on the objective function by using a nonlinear optimization Gauss-Newton algorithm, so that the minimum value is obtained when the objective function is satisfied, and the optimal current robot pose is obtained.
In a possible preferred embodiment, the step of S4 includes:
s41, constructing a laser point cloud map of the area according to all the laser point data and the pose of the mobile robot, and acquiring a local map;
s42, matching the local map with the global map, using an icp algorithm, and simultaneously adding anchor point matching constraints of the local map and the global map to obtain the rotation and translation changes of the precise coordinate system between the local map and the global map;
s43 tiles the local map into the global map and culls duplicate and isolated map portions to update the global map.
In another aspect, the present invention further provides an apparatus for automatically updating a map of a mobile robot, including: the system comprises a data acquisition module, a mapping module, a positioning module and an automatic updating module, wherein the data acquisition module receives laser point data transmitted by a laser radar, extracts anchor point laser point coordinates through a first characteristic threshold value, and then extracts each laser point Pi(i-1, 2, 3 … n) calculating the Euclidean distance between adjacent laser points, when the distance is smaller than a preset threshold and the reflectivity reaches a first characteristic threshold, extracting the laser points belonging to the anchor points through a clustering algorithm, and obtaining the global coordinate of the map where the center position of the anchor point is located in the global map through anchor point shape fitting;
the positioning module acquires laser point data transmitted by the data acquisition module and matches the current laser point cloud map to obtain the real-time position and confidence level of the current mobile robot, and when the confidence level of the fixed position is lower than a set threshold value, the mapping module is activated to start a mapping mode;
the mapping module obtains the laser point data at the current moment, and obtains the current moment t from the positioning moduleiMoving robot pose TiConverting the laser point data according to the current time position and posture to obtain the global laser point coordinate (x)i，yi) Then, the coordinates (reflector _ x) of the laser points belonging to the anchor point are extracted according to the first characteristic thresholdi，reflector_yi) (ii) a Using already existingEstablishing a kdtree for a map coordinate point; respectively searching for the corresponding closest laser spot (map _ x)i，map_yi) And the anchor coordinates in the map (map _ reflector _ x)i，map_reflector_yi) Calculating an optimized objective function f (T)i)＝[(xi-map_xi)2+(yi-map_yi)2+pWeight*((reflector_xi-map_reflector_xi)2+(reflector_yi-map_reflector_yi)2)](ii) a Then, an objective function is iteratively solved by utilizing a nonlinear optimization Gauss-Newton algorithm, so that a minimum value is obtained when the objective function is met, the optimal current mobile robot pose is obtained, and then a laser point cloud map of the area is constructed according to all laser point data and the poses of the mobile robot, so that a local map is obtained;
the automatic updating module obtains a local map to be matched with the global map, uses an icp algorithm and adds anchor point matching constraint of the local map and the global map to obtain the rotation and translation change of a coordinate system between the local map and the global map, then splices the local map into the global map, and rejects repeated and isolated map points to update the global map.
In a possible preferred embodiment, the laser point data comprises: all distances, angle data and reflectivity of the current mobile robot at the moment of the motion position.
In a possible preferred embodiment, the mapping module records the current frame t0Laser pose T0After the laser point data is converted into the current coordinate system, a kdTere is established for the laser point data, and the laser pose T of the next moment ti is estimated by using the uniform motion model V and the laser time interval delta TiWherein T isi＝T0+ V Δ t; then the laser point data at the next moment is processed according to the pose TiMake a conversion for tiEach laser spot (x) at a timei，yi) Searching for the coordinates (Lx) of the current closest point using kdTerei，Lyi) To establish an objective function according to the pose of the current time
Then, the optimal current pose T is obtained by iterative optimization of the objective functioniObtaining a minimum value to satisfy the objective function; and then setting a key frame according to the movement distance and time, setting the laser frame meeting the conditions as the key frame, simultaneously carrying out closed-loop detection, and completing the whole global map building by using a map optimization algorithm.
In a possible preferred embodiment, the anchor point is a reflective pillar, the first identification feature of the anchor point is a reflective characteristic, and the first feature threshold includes: the laser radar reflection column is characterized by comprising at least one of a reflectivity threshold value different from an environment object and a radius threshold value of the reflection column, wherein the reflection column is arranged perpendicular to the ground, and meanwhile, the alignment of the scanning line of the laser radar and the center height of the reflection column is guaranteed.
The method and the device for automatically updating the map of the mobile robot provided by the invention can be suitable for the existing robot which moves by laser navigation, so that the local change part of the map can still be accurately and automatically updated into the original map when the local map is changed due to drastic environment change in the automatic operation process of the robot in a factory environment, thereby ensuring that the accuracy and the effectiveness of map updating can be ensured under the condition that manual work does not participate.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this application, illustrate embodiments of the invention and, together with the description, serve to explain the invention and not to limit the invention. In the drawings:
FIG. 1 is a schematic diagram of a laser point cloud environment map with anchor points according to the present invention;
FIG. 2 is a diagram of the steps of a method for automatically updating a map of a mobile robot according to the present invention;
fig. 3 is a schematic structural diagram of an apparatus for automatically updating a map of a mobile robot according to the present invention.
Detailed Description
The following describes in detail embodiments of the present invention. The following examples will assist those skilled in the art in further understanding the invention, but are not intended to limit the invention in any way. It should be noted that variations and modifications can be made by persons skilled in the art without departing from the spirit of the invention. All falling within the scope of the present invention.
It should be noted that the embodiments and features of the embodiments in the present application may be combined with each other without conflict. The present invention will be described in detail below with reference to the embodiments with reference to the attached drawings.
In order to make those skilled in the art better understand the technical solution of the present invention, the technical solution in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be obtained by a person skilled in the art without any inventive step based on the embodiments of the present invention, shall fall within the scope of the present invention.
It should be noted that the terms "first," "second," and the like in the description and claims of the present invention and in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the invention described herein are capable of operation in sequences other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," as well as any variations thereof, are intended to cover non-exclusive inclusions.
The invention mainly aims to ensure that the mobile robot adopting the existing laser navigation equipment to carry out laser contour positioning navigation can still stably update the map in real time when the navigation environment is changed greatly, and can ensure the accuracy and precision of map updating.
(A)
For this purpose, as shown in fig. 1 and 3, the first aspect of the invention providesAn apparatus for automatically updating a map of a mobile robot is provided, comprising: the system comprises a data acquisition module, a mapping module, a positioning module and an automatic updating module, wherein before the mobile robot runs and updates, a whole environment map can be constructed by using a laser SLAM algorithm to acquire a global map, and in the preferred embodiment of the scheme, the mapping module records a current frame t0Laser pose T0After the laser point data is converted into the current coordinate system, a kdTere is established for the laser point data, and the laser pose T of the next moment ti is estimated by using the uniform motion model V and the laser time interval delta TiWherein T isi＝T0+ V Δ t; then the laser point data at the next moment is processed according to the pose TiMake a conversion for tiEach laser spot (x) at a timei，yi) Searching for the coordinates (Lx) of the current closest point using kdTerei，Lyi) To establish an objective function according to the pose of the current time
Then, the optimal current pose T is obtained by iterative optimization of the objective functioniTo satisfy the objective function, the minimum value is obtained: and then setting a key frame according to the movement distance and time, setting the laser frame meeting the conditions as the key frame, simultaneously carrying out closed-loop detection, and completing the whole global map building by using a map optimization algorithm.
It should be noted that, in order to accurately update some areas in the global map in the following, an anchor point is further set in a preset coordinate position of the global map, where in order to facilitate description of an implementation manner of the present application, in a preferred implementation manner, the anchor point may be a reflective pillar, a first identification feature of the anchor point is a reflective characteristic, and the first feature threshold includes: the laser radar reflection column is characterized by comprising at least one of a reflectivity threshold value and/or a reflection column radius threshold value which are different from the environmental object, wherein the reflection column is arranged perpendicular to the ground, and meanwhile, the alignment of the scanning line of the laser radar and the center height of the reflection column is ensured, so that the reflection column can be well identified.
Because the reflectivity of the reflective column is far away in the environmentThe laser data points belonging to the reflective columns can be extracted by setting the threshold value according to the reflectivity data returned by the laser radar, so that the coordinates (x, y) of the laser data points of all the reflective columns are obtained after the global map is constructed, then all the laser points belonging to the reflective columns are extracted by a clustering algorithm according to the known radius of the reflective columns, and then the coordinates (x, y) of the circle center position of each reflective column can be obtained by fitting0，y0) It can be seen that in this way, all anchor point coordinates are obtained based on the origin of the global map coordinate system.
Then, after the mobile robot enters a map area with a drastically changing environment, the data acquisition module receives laser point data transmitted by the laser radar, wherein the laser point data in this embodiment includes: all distances, angle data and reflectivity of the current mobile robot at the moment of motion position, simultaneously extracting the coordinates of anchor point laser points by a data acquisition module through a first characteristic threshold, then calculating the Euclidean distance between adjacent laser points for each laser point Pi (i is 1, 2, 3 … n), extracting the laser points belonging to the anchor points through a clustering algorithm when the distance is smaller than a preset threshold and the reflectivity reaches the first characteristic threshold, and obtaining the global coordinates of a map where the center position of the anchor point is located in the global map through anchor point shape fitting;
the positioning module acquires laser point data transmitted by the data acquisition module and matches the current laser point cloud map to obtain the real-time position and confidence level of the current mobile robot, and when the confidence level of the fixed position is lower than a set threshold value, the mapping module is activated to start a mapping mode;
the mapping module obtains the laser point data at the current moment, and obtains the current moment t from the positioning moduleiMoving robot pose TiConverting the laser point data according to the current time position and posture to obtain the global laser point coordinate (x)i，yi) Then, the coordinates (reflector _ x) of the laser points belonging to the anchor point are extracted according to the first characteristic thresholdi，reflector_yi) (ii) a Establishing kdtree by using the existing map coordinate points; respectively searching for the corresponding closest laser spot (map _ x)i，map_yi) And anchor in mapPoint coordinate (map _ reflector _ x)i，map_reflector_yi) Calculating an optimized objective function f (T)i)＝[(xi-map_xi)2+(yi-map_yi)2+pWeight*((reflector_xi-map_reflector_xi)2+(reflector_yi-map_reflector_yi)2)](ii) a Then, an objective function is iteratively solved by utilizing a nonlinear optimization Gauss-Newton algorithm, so that a minimum value is obtained when the objective function is met, the optimal current mobile robot pose is obtained, and then a laser point cloud map of the area is constructed according to all laser point data and the poses of the mobile robot, so that a local map is obtained;
the automatic updating module obtains a local map to be matched with the global map, uses an icp algorithm and adds anchor point matching constraint of the local map and the global map to obtain the rotation and translation change of a coordinate system between the local map and the global map, then splices the local map into the global map, and rejects repeated and isolated map points, thus updating the global map.
(II)
Referring to fig. 1 to 2, in order to realize that the navigation map can be accurately and automatically updated even when the local environment is changed drastically, a second aspect of the present invention further provides a method for automatically updating a map of a mobile robot corresponding to the apparatus of the first embodiment, including the steps of:
s1, setting an anchor point at a global coordinate position of a preset map, setting a first identification characteristic for the anchor point, constructing the map by adopting laser point cloud, and screening the anchor point through a preset first characteristic threshold;
s2, obtaining the global coordinate value of the map where the anchor point is located according to a first algorithm;
s3, when the mobile robot detects that the current positioning position is lower than a preset threshold value, starting a mapping mode, and acquiring the pose of the mobile robot according to a second algorithm;
s4, constructing a local map, establishing a map updating block by using the anchor point as a constraint, and replacing the corresponding block of the map to finish updating.
In a possible preferred embodiment, the step of constructing a map by using the laser point cloud in step S1 includes:
s11 records the current frame t0Laser pose T0After the laser point data is converted into the current coordinate system, a kdTere is established for the laser point data, and the laser pose T of the next moment ti is estimated by using the uniform motion model V and the laser time interval delta TiWherein T isi＝T0+ V Δ t, and in addition V is updated according to the calculated pose change;
s12 the laser point data at the next moment is processed according to the pose TiMake a conversion for tiEach laser spot (x) at a timei，yi) Searching for the coordinates (Lx) of the current closest point using kdTerei，Lyi) To establish an objective function according to the pose of the current time
S13 iterative optimization of objective function to obtain optimal current pose TiObtaining a minimum value to satisfy the objective function;
s14 sets a key frame according to the motion distance and time, sets the laser frame meeting the conditions as the key frame, simultaneously carries out closed-loop detection, and completes the whole SLAM mapping by using a mapping optimization algorithm.
Wherein under this embodiment, the anchor point is anti-light post, and its first identification feature is reflection of light characteristic, first characteristic threshold value includes: a reflectivity threshold distinguished from the environment object, and at least one of a reflective column radius threshold.
In this embodiment, the first algorithm step in step S2 includes:
s21, acquiring the distance, angle data and reflectivity of the current mobile robot motion position moment, and extracting the anchor point laser point coordinates of the current moment through a reflectivity threshold; the coordinates of the anchor point laser points are expressed under the current laser local coordinate system;
s22 for each laser point PiAnd (i is 1, 2, 3 … n), calculating the Euclidean distance between adjacent laser points, extracting laser points belonging to the anchor points through a clustering algorithm when the distance is smaller than a preset threshold and the reflectivity reaches a first characteristic threshold, and fitting the coordinates of the laser points in the global map through the shape of the anchor points to obtain the global coordinates of the map where the center positions of the anchor points are located.
In this embodiment, the second algorithm step in step S3 includes:
s31 obtaining the current time tiMoving robot pose TiConverting the laser point data according to the current time position and posture to obtain the global laser point coordinate (x)i，yi)；
S32 extracting the coordinates (reflector _ x) of the laser point belonging to the anchor point according to the first characteristic thresholdi，reflector_yi) (ii) a Establishing kdtree by using the existing map coordinate points; respectively searching for the corresponding closest laser spot (map _ x)i，map_yi) And the anchor coordinates in the map (map _ reflector _ x)i，map_reflector_yi) Calculating an optimized objective function f (T)i)＝[(xi-map_xi)2+(yi-map_yi)2+pWeight*((reflector_xi-map_reflector_xi)2+(reflector_yi-map_reflector_yi)2)]；
And S32, carrying out iterative solution on the objective function by using a nonlinear optimization Gauss-Newton algorithm, so that the minimum value is obtained when the objective function is satisfied, and the optimal current robot pose is obtained.
In this embodiment, the step S4 includes:
s41, constructing a laser point cloud map of the area according to all the laser point data and the pose of the mobile robot, and acquiring a local map;
s42, matching the local map with the global map, using an icp algorithm, and simultaneously adding anchor point matching constraints of the local map and the global map to obtain the rotation and translation changes of the precise coordinate system between the local map and the global map;
s43 tiles the local map into the global map and culls duplicate and isolated map portions to update the global map.
In summary, the method and apparatus for automatically updating a mobile robot map provided by the present invention can extract and obtain the global coordinates of the reflective columns in the laser point cloud map by the reflective columns pre-arranged in the environment, thereby using the global coordinates of the reflective columns as constraints and constructing the local point cloud map by using the laser frame matching algorithm, so as to perform a matching between the local point cloud map and the existing map again, and simultaneously adding the characteristics of the reflective columns as constraints to perform global optimization, thereby obtaining the updated final laser point cloud map, and thus, compared with the conventional grid map, the method of the present invention consumes less computing resources and has faster computing speed, and meanwhile, the method and apparatus can be applied to the existing robot using laser navigation movement, so that when the local map changes caused by the environmental changes occur in the automatic operation process of the factory environment, the local change part of the map can be accurately and automatically updated into the original map, so that the correctness and the effectiveness of map updating can be ensured under the condition that manual work does not participate.
The preferred embodiments of the invention disclosed above are intended to be illustrative only. The preferred embodiments are not intended to be exhaustive or to limit the invention to the precise embodiments disclosed. Obviously, many modifications and variations are possible in light of the above teaching. The embodiments were chosen and described in order to best explain the principles of the invention and the practical application, to thereby enable others skilled in the art to best utilize the invention. The invention is limited only by the claims and the full scope and equivalents thereof, and any modification, equivalent replacement, or improvement made within the spirit and principle of the invention should be included in the protection scope of the invention.
It will be appreciated by those skilled in the art that, in addition to implementing the system, apparatus and various modules thereof provided by the present invention in the form of pure computer readable program code, the same procedures may be implemented entirely by logically programming method steps such that the system, apparatus and various modules thereof provided by the present invention are implemented in the form of logic gates, switches, application specific integrated circuits, programmable logic controllers, embedded microcontrollers and the like. Therefore, the system, the device and the modules thereof provided by the present invention can be considered as a hardware component, and the modules included in the system, the device and the modules thereof for implementing various programs can also be considered as structures in the hardware component; modules for performing various functions may also be considered to be both software programs for performing the methods and structures within hardware components.
In addition, all or part of the steps of the method according to the above embodiments may be implemented by a program instructing related hardware, where the program is stored in a storage medium and includes several instructions to enable a single chip, a chip, or a processor (processor) to execute all or part of the steps of the method according to the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
In addition, any combination of various different implementation manners of the embodiments of the present invention is also possible, and the embodiments of the present invention should be considered as disclosed in the embodiments of the present invention as long as the combination does not depart from the spirit of the embodiments of the present invention.
Claims (10)
1. A method of automatically updating a map of a mobile robot, characterized by the steps of:
s1, setting an anchor point at a global coordinate position of a preset map, setting a first identification characteristic for the anchor point, constructing the map by adopting laser point cloud, and screening the anchor point through a preset first characteristic threshold;
s2, obtaining the global coordinate value of the map where the anchor point is located according to a first algorithm;
s3, when the mobile robot detects that the current positioning position is lower than a preset threshold value, starting a mapping mode, and acquiring the pose of the mobile robot according to a second algorithm;
s4, constructing a local map, establishing a map updating block by using the anchor point as a constraint, and replacing the corresponding block of the map to finish updating.
2. The method for automatically updating the map of the mobile robot according to claim 1, wherein the step of constructing the map by the laser point cloud in the step S1 comprises:
s11 records the current frame t0Laser pose T0After the laser point data is converted into the current coordinate system, a kdTere is established for the laser point data, and the laser pose T of the next moment ti is estimated by using the uniform motion model V and the laser time interval delta TiWherein T isi＝T0+ V Δ t, and in addition V is updated according to the calculated pose change;
s12 the laser point data at the next moment is processed according to the pose TiMake a conversion for tiEach laser spot (x) at a timei，yi) Searching for the coordinates (Lx) of the current closest point using kdTerei，Lyi) To establish an objective function according to the pose of the current time
S13 iterative optimization of objective function to obtain optimal current pose TiObtaining a minimum value to satisfy the objective function;
s14 sets a key frame according to the motion distance and time, sets the laser frame meeting the conditions as the key frame, simultaneously carries out closed-loop detection, and completes the whole SLAM mapping by using a mapping optimization algorithm.
3. The method of automatically updating a map of a mobile robot of claim 1, wherein the anchor point is a retro-reflective post and the first identified feature is a retro-reflective characteristic, the first feature threshold comprising: a reflectivity threshold distinguished from the environment object, and at least one of a reflective column radius threshold.
4. The method for automatically updating a map of a mobile robot according to claim 1, wherein the first algorithm step in the step S2 comprises:
s21, acquiring the distance, angle data and reflectivity of the current mobile robot motion position moment, and extracting the anchor point laser point coordinates of the current moment through a reflectivity threshold; the coordinates of the anchor point laser points are expressed under the current laser local coordinate system;
s22 for each laser point PiAnd (i is 1, 2, 3 … n), calculating the Euclidean distance between adjacent laser points, extracting laser points belonging to the anchor points through a clustering algorithm when the distance is smaller than a preset threshold and the reflectivity reaches a first characteristic threshold, and fitting the coordinates of the laser points in the global map through the shape of the anchor points to obtain the global coordinates of the map where the center positions of the anchor points are located.
5. The method for automatically updating a map of a mobile robot according to claim 1, wherein the second algorithm step in the step S3 comprises:
s31 obtaining the current time tiMoving robot pose TiConverting the laser point data according to the current time position and posture to obtain the global laser point coordinate (x)i，yi)；
S32 extracting the coordinates (reflector _ x) of the laser point belonging to the anchor point according to the first characteristic thresholdi，reflector_yi) (ii) a Establishing kdtree by using the existing map coordinate points; respectively searching for the corresponding closest laser spot (map _ x)i，map_yi) And the anchor coordinates in the map (map _ reflector _ x)i，map_reflector_yi) Calculating an optimized objective function f (T)i)＝[(xi-map_xi)2+(yi-map_yi)2+pWeight*((reflector_xi-map_reflector_xi)2+(reflector_yi-map_reflector_yi)2)]；
And S32, carrying out iterative solution on the objective function by using a nonlinear optimization Gauss-Newton algorithm, so that the minimum value is obtained when the objective function is satisfied, and the optimal current robot pose is obtained.
6. The method of automatically updating a map of a mobile robot as claimed in claim 1, wherein the step of S4 comprises:
s41, constructing a laser point cloud map of the area according to all the laser point data and the pose of the mobile robot, and acquiring a local map;
s42, matching the local map with the global map, using an icp algorithm, and simultaneously adding anchor point matching constraints of the local map and the global map to obtain the rotation and translation changes of the precise coordinate system between the local map and the global map;
s43 tiles the local map into the global map and culls duplicate and isolated map portions to update the global map.
7. An apparatus for automatically updating a map of a mobile robot, comprising: the system comprises a data acquisition module, a mapping module, a positioning module and an automatic updating module, wherein the data acquisition module receives laser point data transmitted by a laser radar, extracts anchor point laser point coordinates through a first characteristic threshold value, and then extracts each laser point Pi(i-1, 2, 3 … n) calculating the Euclidean distance between adjacent laser points, when the distance is smaller than a preset threshold and the reflectivity reaches a first characteristic threshold, extracting the laser points belonging to the anchor points through a clustering algorithm, and obtaining the global coordinate of the map where the center position of the anchor point is located in the global map through anchor point shape fitting;
the positioning module acquires laser point data transmitted by the data acquisition module and matches the current laser point cloud map to obtain the real-time position and confidence level of the current mobile robot, and when the confidence level of the fixed position is lower than a set threshold value, the mapping module is activated to start a mapping mode;
the mapping module obtains the laser point data at the current moment, and obtains the current moment t from the positioning moduleiMoving robot pose TiConverting the laser point data according to the current time position and posture to obtain the global laser point coordinate (x)i，yi) Then, the coordinates (reflector _ x) of the laser points belonging to the anchor point are extracted according to the first characteristic thresholdi，reflector_yi) (ii) a Establishing kdtree by using the existing map coordinate points; respectively searching for the corresponding closest laser spot (map _ x)i，map_yi) And the anchor coordinates in the map (map _ reflector _ x)i，map_reflector_yi) Calculating an optimized objective function f (T)i)＝[(xi-map_xi)2+(yi-map_yi)2+pWeight*((reflector_xi-map_reflector_xi)2+(reflector_yi-map_reflector_yi)2)](ii) a Then, an objective function is iteratively solved by utilizing a nonlinear optimization Gauss-Newton algorithm, so that a minimum value is obtained when the objective function is met, the optimal current mobile robot pose is obtained, and then a laser point cloud map of the area is constructed according to all laser point data and the poses of the mobile robot, so that a local map is obtained; the automatic updating module obtains a local map to be matched with the global map, uses an icp algorithm and adds anchor point matching constraint of the local map and the global map to obtain the rotation and translation change of a coordinate system between the local map and the global map, then splices the local map into the global map, and rejects repeated and isolated map points to update the global map.
8. The apparatus for automatically updating a map of a mobile robot of claim 7, wherein the laser point data comprises: all distances, angle data and reflectivity of the current mobile robot at the moment of the motion position.
9. The apparatus of claim 7, wherein the mapping module records the current frame t0Laser pose T0After the laser point data is converted into the current coordinate system, a kdTere is established for the laser point data, and the laser pose T of the next moment ti is estimated by using the uniform motion model V and the laser time interval delta TiWherein T isi＝T0+ V Δ t; at the next momentLaser point data according to pose TiMake a conversion for tiEach laser spot (x) at a timei，yi) Searching for the coordinates (Lx) of the current closest point using kdTerei，Lyi) To establish an objective function according to the pose of the current time
Then, the optimal current pose T is obtained by iterative optimization of the objective functioniObtaining a minimum value to satisfy the objective function; and then setting a key frame according to the movement distance and time, setting the laser frame meeting the conditions as the key frame, simultaneously carrying out closed-loop detection, and completing the whole global map building by using a map optimization algorithm.
10. The apparatus for automatically updating a map of a mobile robot according to claim 7, wherein the anchor point is a reflective pillar and the first recognition feature is a reflective characteristic, and the first feature threshold comprises: the laser radar reflection column is characterized by comprising at least one of a reflectivity threshold value different from an environment object and a radius threshold value of the reflection column, wherein the reflection column is arranged perpendicular to the ground, and meanwhile, the alignment of the scanning line of the laser radar and the center height of the reflection column is guaranteed.
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