WO2022007385A1 - Procédé et dispositif de fusion de positionnement laser et visuel - Google Patents

Procédé et dispositif de fusion de positionnement laser et visuel Download PDF

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Publication number
WO2022007385A1
WO2022007385A1 PCT/CN2021/072510 CN2021072510W WO2022007385A1 WO 2022007385 A1 WO2022007385 A1 WO 2022007385A1 CN 2021072510 W CN2021072510 W CN 2021072510W WO 2022007385 A1 WO2022007385 A1 WO 2022007385A1
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Prior art keywords
visual
map
positioning
mapping
laser
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PCT/CN2021/072510
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English (en)
Chinese (zh)
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王小挺
白静
程伟
谷桐
张晓凤
陈士凯
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上海思岚科技有限公司
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Publication of WO2022007385A1 publication Critical patent/WO2022007385A1/fr

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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • 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
    • G01C21/005—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 with correlation of navigation data from several sources, e.g. map or contour matching
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • 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
    • G01C21/20—Instruments for performing navigational calculations

Definitions

  • the present application relates to the field of computer technology, and in particular, to a method and device for fusion of laser and visual positioning.
  • SLAM Laser Simultaneous Positioning and Map Construction
  • VSLAM has the advantages of low deployment cost, large amount of information, and wide application range. It is a mainstream direction for future development. However, it also has disadvantages such as being greatly affected by lighting and the built map cannot be used for path planning.
  • the principle of visual label SLAM (TagSLAM) is basically the same as that of conventional VSLAM, but it uses decodable visual labels to replace image feature points, and uses the geometric constraints of visual labels and the uniqueness of encoded values for precise positioning.
  • TagSLAM can attach tags to areas that will not change easily, such as ceilings, so that even if the environment changes greatly, TagSLAM can still maintain the stability of positioning.
  • the disadvantage of TagSLAM is that it requires intrusive deployment to modify the environment and cannot be applied to all scenarios. For some places where tags can be deployed, it is difficult to cover the entire environment.
  • An object of the present application is to provide a method and device for fusion of laser and visual positioning, which solves the problems in the prior art that the stability of the existing positioning method is difficult to maintain and cannot be applied to all scenarios.
  • a method for fusion of laser and visual positioning comprising:
  • the visual mapping engine When the visual mapping engine is in the positioning mode, all visual label sub-images are screened, and the positioning quality is determined according to the screened visual label sub-images and the three-dimensional transformation relationship;
  • the visual positioning observation input information is determined according to the positioning quality, and the visual positioning observation input information is fused with the laser positioning information and the odometer information.
  • calculating the three-dimensional transformation relationship between each visual label sub-map and the corresponding first map including:
  • the three-dimensional transformation relationship between each visual label sub-map and the corresponding first map is calculated according to the corresponding relationship.
  • calculating the three-dimensional transformation relationship between each visual label sub-map and the corresponding first map according to the corresponding relationship including:
  • the positioning quality is determined according to the screened visual label submap and the three-dimensional transformation relationship, including:
  • the positioning quality is determined according to the mapping quality of the visual mapping.
  • determining the positioning quality according to the mapping quality of the visual mapping including:
  • the positioning quality is determined according to the construction quality of the visual mapping, the number of visual labels, and the threshold of the number of visual labels.
  • the relationship between the transformed visual mapping key frame and the corresponding laser mapping key frame is calculated. average distance.
  • determining the visual positioning observation input information according to the positioning quality including:
  • laser mapping as the main system, polling the current pose of the device where the visual mapping engine is located through process communication, to determine whether the current pose is a valid pose;
  • the current pose is preprocessed according to the positioning quality, and the visual positioning observation input information is determined according to the preprocessing result.
  • a laser and visual positioning fusion device comprising:
  • an acquiring device for acquiring a first map established by a laser mapping engine and a second map established by a visual mapping engine, wherein the second map includes a plurality of visual label sub-maps;
  • a computing device for computing the three-dimensional transformation relationship between each visual label sub-map and the corresponding first map
  • a determination device used for screening all the visual label sub-maps when the visual mapping engine is in the positioning mode, and determining the positioning quality according to the screened effective visual label sub-maps and the three-dimensional transformation relationship;
  • the fusion device is configured to determine the visual positioning observation input information according to the positioning quality, and fuse the visual positioning observation input information with the laser positioning information and the odometer information.
  • a computer-readable medium having computer-readable instructions stored thereon, the computer-readable instructions being executable by a processor to implement the method as described above.
  • the present application obtains a first map established by a laser mapping engine and a second map established by a visual mapping engine, wherein the second map includes a plurality of visual label sub-maps; A three-dimensional transformation relationship between a visual label sub-map and the corresponding first map; when the visual mapping engine is in the positioning mode, all visual label sub-maps are screened, according to the filtered visual label sub-map and the three-dimensional transformation relationship
  • the positioning quality is determined;
  • the visual positioning observation input information is determined according to the positioning quality, and the visual positioning observation input information is fused with the laser positioning information and the odometer information. Therefore, through the collaborative work of the laser mapping engine and the visual mapping engine, the stability of positioning can be maintained and the scope of applicable application scenarios can be expanded.
  • FIG. 1 shows a schematic flowchart of a method for fusion of laser and visual positioning provided according to an aspect of the present application
  • FIG. 2 shows a schematic flowchart of mapping in a specific embodiment of the present application
  • FIG. 3 shows a schematic flowchart of a method for fusion of laser and visual positioning based on an extended Kalman filter in a specific embodiment of the present application
  • FIG. 4 shows a schematic structural diagram of a laser and visual positioning fusion device provided by another aspect of the present application.
  • the terminal, the device serving the network, and the trusted party all include one or more processors (for example, a central processing unit (CPU)), an input/output interface, a network interface, and a memory .
  • processors for example, a central processing unit (CPU)
  • Memory may include non-persistent memory in computer-readable media, random access memory (RAM) and/or non-volatile memory, such as read only memory (ROM) or flash memory ( flash RAM). Memory is an example of a computer-readable medium.
  • RAM random access memory
  • ROM read only memory
  • flash RAM flash memory
  • Computer-readable media includes both persistent and non-permanent, removable and non-removable media, and storage of information may be implemented by any method or technology.
  • Information may be computer readable instructions, data structures, modules of programs, or other data.
  • Examples of computer storage media include, but are not limited to, phase-change memory (Phase-Change RAM, PRAM), static random access memory (Static Random Access Memory, SRAM), dynamic random access memory (Dynamic Random Access Memory, DRAM) , other types of random access memory (RAM), read only memory (ROM), Electrically Erasable Programmable Read-Only Memory (EEPROM), flash memory or other memory technology, only Compact Disc Read-Only Memory (CD-ROM), Digital Versatile Disk (DVD) or other optical storage, magnetic cassette, magnetic tape storage or other magnetic storage device or any other A non-transmission medium that can be used to store information that can be accessed by a computing device.
  • computer-readable media does not include non-transitory computer-readable media (transitory media), such as
  • FIG. 1 shows a schematic flowchart of a method for laser and visual positioning fusion provided according to an aspect of the present application.
  • the method includes: steps S11 to S14,
  • step S11 the first map established by the laser mapping engine and the second map established by the visual mapping engine are obtained, wherein the second map includes a plurality of visual label sub-maps; here, the laser mapping engine
  • the map construction is laser SLAM construction
  • the created map is the first map
  • the first map is a grid map
  • the visual mapping engine construction is TagSLAM mapping
  • the created map is the second map, when the second map is created
  • the map is only built in the area with the label
  • the area where the label is located is a subgraph.
  • several visual label subgraphs are obtained. The subgraph can greatly expand the application scope of visual label SLAM.
  • step S12 the three-dimensional transformation relationship between each visual label sub-map and the corresponding first map is calculated; here, TagSLAM builds a map in the form of a sub-map, and each visual label sub-map is completed and the corresponding first map is calculated.
  • the three-dimensional transformation relationship of the map, the transformation relationship is the rotation matrix r and the translation vector t.
  • step S13 when the visual mapping engine is in the positioning mode, all visual label sub-pictures are screened, and the positioning quality is determined according to the screened visual label sub-picture and the three-dimensional transformation relationship;
  • open TagSLAM When TagSLAM is in the positioning mode, all observed visual labels are screened to filter out valid visual labels, that is, if an effective visual label is observed, it is obtained according to the subgraph where the effective visual label is located and the above calculation.
  • the positioning quality is determined by the three-dimensional transformation relationship of , and the positioning quality is used to describe the reliability of positioning.
  • the visual positioning quality is between 0 and 1, and the larger the value, the more reliable it is.
  • step S14 visual positioning observation input information is determined according to the positioning quality, and the visual positioning observation input information is fused with laser positioning information and odometer information.
  • the positioning quality it is determined that the visual positioning observation input information is the current pose, whether it needs to be repositioned to obtain a new pose, or the current pose is not fused; the determined visual positioning observation input information is combined with laser positioning.
  • odometry for fusion can use Extended Kalman Filter (EKF), and of course, can also access other positioning information, such as GPS, third-party VSLAM positioning, EKF can handle nonlinear systems, so that each source is different
  • EKF Extended Kalman Filter
  • step S12 the corresponding relationship between the visual mapping key frame in the second map and the laser mapping key frame in the first map is recorded according to the timestamp; The three-dimensional transformation relationship between each visual label sub-map and the corresponding first map.
  • laser SLAM and TagSLAM build maps independently.
  • laser SLAM pushes key frames to TagSLAM in real time.
  • TagSLAM records the one-to-one correspondence between the key frames in the two maps according to the timestamp when building the map.
  • the first frame after that is a visual key frame; if the position of the observed label is compared with the previous frame, the distance is greater than the preset pixel (such as 18 pixels) , the selected key frame will participate in the mapping, but there is no corresponding laser key frame, and will not participate in the subsequent calculation of the 3D transformation of the sub-image.
  • the three-dimensional transformation from the TagSLAM map to the laser SALM map is calculated by the ICP (Iterative Closest Point) algorithm.
  • the visual label sub-map refers to a local map created by a visual mapping engine, and several visual label sub-maps in the second map are independent of each other. Transform the relationship to get the pose of each visual label subgraph in the world coordinate system.
  • the position of each visual label sub-map can be calculated according to the corresponding relationship; the three-dimensional transformation relationship is obtained by calculating the three-dimensional transformation from the position of each visual label sub-map to the corresponding first map.
  • a point cloud matching algorithm is used to obtain the three-dimensional transformation of each visual label sub-image to the corresponding first map.
  • step S13 all visual labels observed when the visual mapping engine is in the positioning mode are obtained; all visual labels are decoded to obtain legal coding values, and all visual label submaps are judged Whether there is a visual label sub-graph containing the coded value, and if so, the visual label sub-graph containing the coded value is used as the filtered visual label sub-graph.
  • a visual label belongs to only one subgraph, and a subgraph contains multiple visual labels; when TagSLAM is in the positioning mode, it filters all the observed visual labels to filter out valid visual labels, that is, if the observed effective visual labels are , the current pose is calculated according to the corresponding visual label subgraph and the three-dimensional transformation relationship.
  • the determination of a valid visual label is as follows: if the visual label is decoded to obtain a legal code value, and the code value is in a certain visual label sub-graph, then the visual label is a valid visual label and participates in the positioning. The subgraph and 3D transformation relationship where the valid visual labels are located are located.
  • step S13 the average distance between the transformed corresponding key frames is determined according to the screened visual label sub-image and the three-dimensional transformation relationship; the average distance is normalized
  • the mapping quality of the visual mapping is obtained; the positioning quality is determined according to the mapping quality of the visual mapping.
  • the screened visual label sub-graph is an effective visual label sub-graph, the average distance between the corresponding key frames after conversion is calculated, and then the average distance is normalized to evaluate the quality of the mapping.
  • the mapping quality is the positioning quality.
  • the pose of the visual mapping key frame in the valid visual label sub-map and the pose of the laser mapping key frame in the first map corresponding to the valid visual label sub-map can be obtained; according to the visual mapping key
  • the pose of the frame, the pose of the laser mapping key frame, and the three-dimensional transformation relationship are used to calculate the average distance between the transformed visual mapping key frame and the corresponding laser mapping key frame.
  • the mean absolute error between the transformed corresponding keyframes is calculated according to the following formula:
  • TKi is the pose of the ith key frame of TagSLAM
  • LKi is the pose of the ith key frame of laser SLAM.
  • q is the mapping quality, and the closer the value is to 1, the smaller the error.
  • the positioning quality may be determined according to the construction quality of the visual mapping, the number of visual labels, and the threshold of the number of visual labels.
  • the positioning quality is used to describe the reliability of the positioning.
  • the visual positioning quality is between 0 and 1. The larger the value, the more reliable the positioning quality.
  • step S14 the current pose of the device where the visual mapping engine is located is determined; laser mapping is used as the main system, and the current position of the device where the visual mapping engine is located is polled through process communication pose, determine whether the current pose is a valid pose; when the current pose is a valid pose, preprocess the current pose according to the positioning quality, and determine the visual positioning observation according to the preprocessing result Enter information.
  • the laser SLAM is positioned through an algorithm, and both the positioning result and the odometry value are input into the extended Kalman filter as the observation value; TagSLAM is independently positioned according to the observed result, where the observation result is decoded according to the image captured by the camera.
  • the visual positioning of TagSLAM shall prevail, and the current pose can be directly corrected to obtain the final positioning.
  • the default value of the visual positioning quality can be 0.6, and when it is greater than 0.6, it is considered that the positioning quality is high, and it participates in the fusion process of the extended Kalman filter.
  • the present application uses TagSLAM mapping and laser SLAM mapping, and laser SLAM pushes the laser key frames of the obtained map to TagSLAM mapping, and the The keyframes of TagSLAM and the keyframes of the laser are in one-to-one correspondence according to the time stamp.
  • the global BundleAdjustment (beam adjustment method) is performed.
  • the 2D image feature points are reprojected back to In the 3D domain, there will be a deviation from the position of the real 3D point, and BundleAdjustment is used to minimize this deviation through algorithms such as the least squares method, so as to obtain the precise value of the robot pose.
  • the global BundleAdjustment is optimized for a visual label subgraph as a whole.
  • the three-dimensional transformation relationship between the visual label sub-image and the laser SLAM map is calculated according to the key frame correspondence.
  • FIG. 3 it is a schematic flowchart of a method for fusion of laser and visual positioning based on extended Kalman filter. If it is, then calculate the current pose and save the timestamp.
  • Laser SLAM polls the TagSLAM pose through inter-process communication to determine whether the polled pose is a valid positioning, if not, ignore and do not perform subsequent fusion, if so, Then judge whether the positioning quality is higher than the threshold. If not, a low positioning alarm will be generated and the current pose will not be fused. If so, it will be judged whether the distance from the current pose is large and the laser positioning quality is low, so as to determine whether it is necessary to directly correct the final position. Positioning, if not, it is used as the observation input information of the extended Kalman filter, and the positioning result obtained by SLAM positioning and the value of the odometer are fused in the extended Kalman filter to obtain the final positioning result.
  • an embodiment of the present application further provides a computer-readable medium on which computer-readable instructions are stored, and the computer-readable instructions can be executed by a processor to implement the foregoing method for laser and visual positioning fusion.
  • the present application also provides a terminal, which includes modules or units capable of executing the method steps described in FIG. 1 or FIG. 2 or FIG. It can be implemented by means of hardware, software or a combination of software and hardware, which is not limited in this application.
  • a device for fusion of laser and visual positioning is also provided, and the device includes:
  • a memory storing computer readable instructions which, when executed, cause the processor to perform the operations of the method as previously described.
  • computer-readable instructions when executed, cause the one or more processors to:
  • a method for fusion of laser and visual positioning characterized in that the method comprises:
  • the visual mapping engine When the visual mapping engine is in the positioning mode, all visual label sub-images are screened, and the positioning quality is determined according to the screened visual label sub-images and the three-dimensional transformation relationship;
  • the visual positioning observation input information is determined according to the positioning quality, and the visual positioning observation input information is fused with the laser positioning information and the odometer information.
  • FIG. 4 shows a schematic structural diagram of a laser and visual positioning fusion device provided by another aspect of the present application.
  • the device includes: an acquisition device 11, a computing device 12, a determination device 13, and a fusion device 14.
  • the acquisition device 11 uses For obtaining the first map established by the laser mapping engine and the second map established by the visual mapping engine, wherein, the second map includes a plurality of visual label sub-maps; the computing device 12 is used to calculate each visual label sub-map.
  • the content executed by the acquiring device 11 , the computing device 12 , the determining device 13 and the fusion device 14 is the same or correspondingly the same as the content in the above steps S11 , S12 , S13 and S14 respectively. Repeat.
  • the present application may be implemented in software and/or a combination of software and hardware, for example, may be implemented using an application specific integrated circuit (ASIC), a general purpose computer, or any other similar hardware device.
  • ASIC application specific integrated circuit
  • the software program of the present application may be executed by a processor to implement the steps or functions described above.
  • the software programs of the present application (including associated data structures) may be stored on a computer-readable recording medium, such as RAM memory, magnetic or optical drives or floppy disks, and the like.
  • some steps or functions of the present application may be implemented in hardware, for example, as a circuit that cooperates with a processor to perform various steps or functions.
  • a part of the present application can be applied as a computer program product, such as computer program instructions, which when executed by a computer, through the operation of the computer, can invoke or provide methods and/or technical solutions according to the present application.
  • the program instructions for invoking the methods of the present application may be stored in fixed or removable recording media, and/or transmitted via data streams in broadcast or other signal-bearing media, and/or stored in accordance with the in the working memory of the computer device on which the program instructions are executed.
  • an embodiment according to the present application includes an apparatus comprising a memory for storing computer program instructions and a processor for executing the program instructions, wherein, when the computer program instructions are executed by the processor, a trigger is
  • the apparatus operates based on the aforementioned methods and/or technical solutions according to various embodiments of the present application.

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Abstract

Procédé et dispositif de fusion de positionnement laser et visuel. Le procédé consiste : à acquérir des premières cartes établies par un moteur de cartographie laser et une seconde carte établie par un moteur de cartographie visuelle, la seconde carte comprenant une pluralité de sous-cartes d'étiquette visuelle (S11) ; à calculer une relation de transformation tridimensionnelle entre chaque sous-carte d'étiquette visuelle et une première carte correspondante (S12) ; à déterminer une sous-carte d'étiquette visuelle valide lorsque le moteur de cartographie visuelle se trouve dans un mode de positionnement, et à déterminer une qualité de positionnement en fonction de la sous-carte d'étiquette visuelle valide et de la relation de transformation tridimensionnelle (S13) ; et à déterminer des informations d'entrée d'observation de positionnement visuel en fonction de la qualité de positionnement, et à fusionner les informations d'entrée d'observation de positionnement visuel à des informations de positionnement laser et à des informations d'odomètre (S14). Grâce au travail coopératif d'un moteur de cartographie laser et d'un moteur de cartographie visuelle, la stabilité de positionnement peut être maintenue, et la plage de scénarios d'application applicables peut être étendue.
PCT/CN2021/072510 2020-07-09 2021-01-18 Procédé et dispositif de fusion de positionnement laser et visuel WO2022007385A1 (fr)

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