CN113984065A - Reflector map generation method and system for indoor robot - Google Patents
Reflector map generation method and system for indoor robot Download PDFInfo
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- 238000000034 method Methods 0.000 title claims abstract description 42
- 230000008859 change Effects 0.000 claims abstract description 8
- 238000004364 calculation method Methods 0.000 claims description 8
- 238000003860 storage Methods 0.000 claims description 8
- 238000000605 extraction Methods 0.000 claims description 5
- 238000005457 optimization Methods 0.000 claims description 5
- 238000013507 mapping Methods 0.000 claims 1
- 238000010276 construction Methods 0.000 abstract description 10
- 238000010586 diagram Methods 0.000 description 9
- 238000004590 computer program Methods 0.000 description 8
- 230000036544 posture Effects 0.000 description 6
- 230000008569 process Effects 0.000 description 5
- 230000006870 function Effects 0.000 description 4
- 238000007621 cluster analysis Methods 0.000 description 2
- 230000007547 defect Effects 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
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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
- G01C21/20—Instruments for performing navigational calculations
- G01C21/206—Instruments for performing navigational calculations specially adapted for indoor navigation
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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
- G01C21/10—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration
- G01C21/12—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning
- G01C21/16—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning by integrating acceleration or speed, i.e. inertial navigation
- G01C21/165—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning by integrating acceleration or speed, i.e. inertial navigation combined with non-inertial navigation instruments
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C22/00—Measuring distance traversed on the ground by vehicles, persons, animals or other moving solid bodies, e.g. using odometers, using pedometers
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S17/00—Systems using the reflection or reradiation of electromagnetic waves other than radio waves, e.g. lidar systems
- G01S17/86—Combinations of lidar systems with systems other than lidar, radar or sonar, e.g. with direction finders
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S17/00—Systems using the reflection or reradiation of electromagnetic waves other than radio waves, e.g. lidar systems
- G01S17/88—Lidar systems specially adapted for specific applications
- G01S17/89—Lidar systems specially adapted for specific applications for mapping or imaging
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S17/00—Systems using the reflection or reradiation of electromagnetic waves other than radio waves, e.g. lidar systems
- G01S17/88—Lidar systems specially adapted for specific applications
- G01S17/93—Lidar systems specially adapted for specific applications for anti-collision purposes
Abstract
The invention provides a reflector map generation method and a reflector map generation system for an indoor robot, which are used for acquiring map data of an indoor space; obtaining the current pose of the indoor robot according to the obtained map data and the laser radar data of the indoor robot; according to the light intensity change rate in the laser radar data, combining the current pose of the indoor robot to obtain the pose data of the reflector and storing the pose data into the corresponding reflector sub-table; sequentially extracting reflector position and pose data when the indoor robot is positioned at different positions to obtain all reflector sublists, and combining the reflector position and pose data in all the reflector sublists to obtain a complete reflector point cloud map; the method greatly reduces the steps of on-site construction of the reflector map building and improves the map building precision of the reflector.
Description
Technical Field
The invention relates to the technical field of high-precision positioning of indoor mobile robots, in particular to a reflector map generation method and a reflector map generation system for an indoor robot.
Background
The statements in this section merely provide background information related to the present disclosure and may not constitute prior art.
The map construction technology is the basis for realizing autonomous navigation of the robot, and as the application environment is increasingly complex, the mobile robot has many defects in the aspects of positioning precision, algorithm robustness and complex scene adaptability, and the robot is difficult to accurately and efficiently complete tasks.
The inventor finds that the reflector map has high positioning accuracy, strong anti-interference capability and wide application prospect. However, in most practical conditions, a large number of reflectors are laid, the position of each reflector needs to be accurately measured, the precision is not high, the intelligent level is low, and the problem of mismatching with the traditional laser SLAM map exists.
Disclosure of Invention
In order to overcome the defects of the prior art, the invention provides a reflector map generation method and a reflector map generation system for an indoor robot, which greatly reduce the reflector map construction field construction steps and improve the reflector map construction precision.
In order to achieve the purpose, the invention adopts the following technical scheme:
the invention provides a reflector map generation method for an indoor robot.
A reflector map generation method for an indoor robot, comprising the processes of:
acquiring map data of an indoor space;
obtaining the current pose of the indoor robot according to the obtained map data and the laser radar data of the indoor robot;
according to the light intensity change rate in the laser radar data, combining the current pose of the indoor robot to obtain the pose data of the reflector and storing the pose data into the corresponding reflector sub-table;
and sequentially extracting the reflector position and pose data when the indoor robot is positioned at different positions to obtain all reflector sub-tables, and combining the reflector position and pose data in all the reflector sub-tables to obtain a complete reflector point cloud map.
Further, when the number of times of the reflector in the same pose is larger than a set threshold, storing the pose data into the corresponding reflector sub-table, combining reflector pose points in the reflector sub-table into frame data after the reflector sub-table is finished, matching the frame data with map data to obtain corrected pose data, and obtaining a corrected sub-table according to the corrected pose data.
Furthermore, when the reflector appears for a plurality of times in the same pose and the appearance times are larger than a set threshold value, the pose is recorded as reflector data to be stored and accumulated for counting, and when the accumulated reflector pose data reaches a set value, the group of data is stored as a reflector sub-table.
Furthermore, after a reflector sublist is formed, the pose data of all reflectors in the group of data are taken to combine into a frame of data set to be matched, the data set to be matched is matched with map data to obtain optimized pose data, and the pose of the reflector sublist is updated again based on the optimized pose data to obtain the optimized reflector sublist.
Furthermore, the optimized reflector sub-tables are subjected to overall optimization based on a nonlinear fitting mode, and all reflector data are updated according to the sub-table poses obtained through optimization.
Further, the pose of the reflector corresponding to the point cloud is determined by combining the current pose of the indoor robot and the light intensity characteristic extraction result of the point cloud data;
determining a point cloud cluster corresponding to the reflector by adopting a clustering method based on light intensity density;
and according to the point cloud cluster to which the reflector belongs, taking the position-posture mean value of the point cloud cluster as the position posture of the reflector, and storing the position posture mean value into a corresponding reflector sub-table.
Further, according to the size of the indoor range, position and attitude point data in the reflector sub-table are combined to form a plurality of map data;
splicing a plurality of maps into one map by adopting nonlinear fitting processing according to the spatial pose relationship of the plurality of map data;
and carrying out nonlinear pose fitting on the point cloud data of the whole map data and the indoor whole grid map to obtain a reflector point cloud map matched with the grid.
A second aspect of the present invention provides a reflector map generation system for an indoor robot.
A reflector map generation system for an indoor robot, comprising:
a data acquisition module configured to: acquiring map data of an indoor space;
a robot pose calculation module configured to: obtaining the current pose of the indoor robot according to the obtained map data and the laser radar data of the indoor robot;
a reflector pose calculation module configured to: according to the light intensity change rate in the laser radar data, combining the current pose of the indoor robot to obtain the pose data of the reflector and storing the pose data into the corresponding reflector sub-table;
a reflector map generation module configured to: and sequentially extracting the reflector position and pose data when the indoor robot is positioned at different positions to obtain all reflector sub-tables, and combining the reflector position and pose data in all the reflector sub-tables to obtain a complete reflector point cloud map.
A third aspect of the present invention provides a computer-readable storage medium having a program stored thereon, wherein the program, when executed by a processor, implements the steps in the reflector map generation method for an indoor robot according to the first aspect of the present invention.
A fourth aspect of the present invention provides an electronic device, comprising a memory, a processor, and a program stored on the memory and executable on the processor, wherein the processor executes the program to implement the steps of the method for generating a reflector map for an indoor robot according to the first aspect of the present invention.
Compared with the prior art, the invention has the beneficial effects that:
the method, the system, the medium or the electronic equipment calculate the pose data of the laser point cloud, extract the characteristic pose point cloud of the reflector according to the pose data of the laser point cloud, calculate the exact pose of each reflector subgraph by adopting a nonlinear fitting mode based on the point cloud data of the reflector and the corresponding pose, match the different reflector subgraphs into a complete map, match the grid map corresponding to the environment, optimize and update the obtained final reflector map, greatly reduce the reflector map construction field construction steps and improve the reflector map construction precision.
Advantages of additional aspects of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, are included to provide a further understanding of the invention, and are incorporated in and constitute a part of this specification, illustrate exemplary embodiments of the invention and together with the description serve to explain the invention and not to limit the invention.
Fig. 1 is a schematic flow chart of a reflector map generation method for an indoor robot according to embodiment 1 of the present invention.
Fig. 2 is a schematic diagram illustrating the creation and optimization of a reflector subgraph provided in embodiment 1 of the present invention
Detailed Description
The invention is further described with reference to the following figures and examples.
It is to be understood that the following detailed description is exemplary and is intended to provide further explanation of the invention as claimed. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of exemplary embodiments according to the invention. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, and it should be understood that when the terms "comprises" and/or "comprising" are used in this specification, they specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof, unless the context clearly indicates otherwise.
The embodiments and features of the embodiments of the present invention may be combined with each other without conflict.
Example 1:
as shown in fig. 1, an embodiment 1 of the present invention provides a method for generating a reflector map for an indoor robot, which utilizes reflectivity characteristics of a laser radar sensor, and implements identification of reflector data through feature extraction and cluster analysis, thereby implementing automatic creation of a reflector map, and specifically includes the following processes:
s1: method for establishing operation environment map by adopting simultaneous positioning and map construction
S1.1: scanning the working environment by using a laser radar, and acquiring data of a speedometer and a gyroscope at the same time;
s1.2: and creating a map under the working environment by using a simultaneous localization and map construction method.
Specifically, the gyroscope provides the angle, the angular velocity and the linear acceleration of the movement of the indoor robot, the encoder adopts an incremental rotary encoder for providing wheel change increment, based on the increment data, a pose increment is obtained through a differential model, a laser radar is used as a laser sensor for scanning a working environment, and the profile characteristic of the working environment and the characteristic of a reflector plate corresponding to light intensity are obtained through the laser radar. Based on the data of the three sensors, the environmental information of the indoor robot is expressed to obtain a map under the working environment.
S2: matching with maps using lidar data
And matching the data of the laser radar with the map established in the S1 to obtain the matching pose of the indoor robot, wherein the matching pose is the coordinate value of the robot relative to the map.
In this embodiment, the pose of the indoor robot is determined by natural laser positioning, and the pose of the reflector on the map is obtained by superimposing the relative pose of the laser radar to the reflector on the pose of the laser radar relative to the indoor robot.
S3: the laser point cloud feature extraction and cluster analysis method obtains the pose data of the reflector:
s3.1: based on the matching pose obtained in S2, performing light intensity feature extraction on the point cloud data of the laser radar, and determining the pose of the point cloud corresponding to the reflector;
s3.2: determining a point cloud cluster corresponding to the reflector by adopting a clustering method based on light intensity density;
s3.3: and analyzing the point cloud cluster to which the reflector belongs to obtain the mean value of the position and posture of the point cloud cluster, taking the mean value as the position and posture of the reflector, and storing the mean value into a corresponding reflector list.
Specifically, the slope is obtained by each point in the point cloud according to the light intensity, and the point with large change of the point cloud light intensity is obtained as the reference point of the position and posture of the reflector through threshold judgment; and acquiring a point cloud cluster near the characteristic points as the integral reflector point cloud according to a density-based mode.
S4: reflector map creation
S4.1: and combining the position and pose point data in the partial reflector list according to the range size to form a plurality of map data. Specifically, the range refers to a movement range of the robot, and after the robot moves for a certain distance, the positions and postures of the reflector in the movement range are combined to form a map.
S4.2: splicing a plurality of maps into a map by adopting nonlinear fitting processing according to the spatial pose relationship of the plurality of map data to be optimized;
s4.3: and carrying out nonlinear pose fitting processing on the point cloud data of the whole map data and the whole grid map to obtain a new reflector point cloud map matched with the grid.
And the nonlinear fitting processing is to construct a residual error model, construct an error equation between the point cloud pose to be optimized and the target point cloud, and input data such as a timestamp, encoder pulses, indoor robot chassis parameters, IMU angular velocity and the like into the residual error model to obtain a variable to be optimized. And giving an initial value through the current pose of the indoor robot, calculating, revising the current pose by using the calculation result, and performing iterative operation until the calculation result is small enough.
The map stitching in this embodiment is to stitch a plurality of maps into a same map by using the pose information of the maps as the matching initial pose and using a nonlinear fitting manner to obtain the spatial pose relationship between the maps.
Example 2:
an embodiment 2 of the present invention provides a reflector map generation system for an indoor robot, including:
a data acquisition module configured to: acquiring map data of an indoor space;
a robot pose calculation module configured to: obtaining the current pose of the indoor robot according to the obtained map data and the laser radar data of the indoor robot;
a reflector pose calculation module configured to: according to the light intensity change rate in the laser radar data, combining the current pose of the indoor robot to obtain the pose data of the reflector and storing the pose data into the corresponding reflector sub-table;
a reflector map generation module configured to: and sequentially extracting the reflector position and pose data when the indoor robot is positioned at different positions to obtain all reflector sub-tables, and combining the reflector position and pose data in all the reflector sub-tables to obtain a complete reflector point cloud map.
The working method of the system is the same as the method for generating the reflector map for the indoor robot provided in embodiment 1, and details are not repeated here.
Example 3:
embodiment 3 of the present invention provides a computer-readable storage medium having a program stored thereon, wherein the program, when executed by a processor, implements the steps in the reflector map generation method for an indoor robot according to embodiment 1 of the present invention.
Example 4:
embodiment 4 of the present invention provides an electronic device, which includes a memory, a processor, and a program stored in the memory and executable on the processor, where the processor executes the program to implement the steps in the reflector map generation method for an indoor robot according to embodiment 1 of the present invention.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of a hardware embodiment, a software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by a computer program, which can be stored in a computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. The storage medium may be a magnetic disk, an optical disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), or the like.
The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (10)
1. A reflector map generation method for an indoor robot is characterized in that:
the method comprises the following steps:
acquiring map data of an indoor space;
obtaining the current pose of the indoor robot according to the obtained map data and the laser radar data of the indoor robot;
according to the light intensity change rate in the laser radar data, combining the current pose of the indoor robot to obtain the pose data of the reflector and storing the pose data into the corresponding reflector sub-table;
and sequentially extracting the reflector position and pose data when the indoor robot is positioned at different positions to obtain all reflector sub-tables, and combining the reflector position and pose data in all the reflector sub-tables to obtain a complete reflector point cloud map.
2. The reflector map generation method for an indoor robot according to claim 1, characterized in that:
and when the occurrence frequency of the reflectors with the same pose is greater than a set threshold value, storing the pose data into the corresponding reflector sub-tables, combining reflector pose points in the reflector sub-tables into frame data after the reflector sub-tables are finished, matching the frame data with map data to obtain corrected pose data, and obtaining the corrected sub-tables according to the corrected pose data.
3. The reflector map generation method for an indoor robot according to claim 2, characterized in that:
when the reflector appears for a plurality of times in the same pose and the appearance times are larger than a set threshold value, recording the pose as reflector data, storing and accumulating the reflector data, and when the accumulated reflector pose data reaches a set value, storing the group of data as a reflector sub-table.
4. The reflector map generation method for an indoor robot according to claim 3, wherein:
and after a reflector sublist is formed, all reflector pose data in the group of data are taken to form a frame of data set to be matched, the data set to be matched is matched with map data to obtain optimized pose data, and the pose of the reflector list is updated again based on the optimized pose data to obtain the optimized reflector sublist.
5. The reflector map generation method for an indoor robot according to claim 4, wherein:
and performing integral optimization on all optimized reflector sub-tables based on a nonlinear fitting mode, and updating all reflector data according to the sub-table poses obtained by optimization.
6. The reflector map generation method for an indoor robot according to claim 1, characterized in that:
determining the pose of the point cloud corresponding to the reflector by combining the current pose of the indoor robot and the light intensity characteristic extraction result of the point cloud data;
determining a point cloud cluster corresponding to the reflector by adopting a clustering method based on light intensity density;
and according to the point cloud cluster to which the reflector belongs, taking the position-posture mean value of the point cloud cluster as the position posture of the reflector, and storing the position posture mean value into a corresponding reflector sub-table.
7. The reflector map generation method for an indoor robot according to claim 1, characterized in that:
according to the moving range of the robot, position and pose point data in the reflector sub-table are combined to form a plurality of map data;
splicing a plurality of maps into one map by adopting nonlinear fitting processing according to the spatial pose relationship of the plurality of map data;
and carrying out nonlinear pose fitting on the point cloud data of the whole map data and the indoor whole grid map to obtain a reflector point cloud map matched with the grid.
8. A reflector map generation system for an indoor robot, characterized by:
the method comprises the following steps:
a data acquisition module configured to: acquiring map data of an indoor space;
a robot pose calculation module configured to: obtaining the current pose of the indoor robot according to the obtained map data and the laser radar data of the indoor robot;
a reflector pose calculation module configured to: according to the light intensity change rate in the laser radar data, combining the current pose of the indoor robot to obtain the pose data of the reflector and storing the pose data into the corresponding reflector sub-table;
a reflector map generation module configured to: and sequentially extracting the reflector position and pose data when the indoor robot is positioned at different positions to obtain all reflector sub-tables, and combining the reflector position and pose data in all the reflector sub-tables to obtain a complete reflector point cloud map.
9. A computer-readable storage medium, on which a program is stored, which, when being executed by a processor, carries out the steps of the method for reflector mapping for an indoor robot of any one of claims 1 to 7.
10. An electronic device comprising a memory, a processor and a program stored on the memory and executable on the processor, wherein the processor implements the steps of the method of any one of claims 1-7 when executing the program.
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| CN111307168A (en) * | 2020-03-19 | 2020-06-19 | 苏州艾吉威机器人有限公司 | AGV (automatic guided vehicle) map building method and positioning method and system |
| CN111694013A (en) * | 2020-06-11 | 2020-09-22 | 三一机器人科技有限公司 | Method, device and equipment for constructing map of reflective column and storage medium |
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