CN110645974A - Mobile robot indoor map construction method fusing multiple sensors - Google Patents
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Abstract
The invention discloses a construction method of an indoor map of a mobile robot fusing multiple sensors, which comprises the following steps: respectively acquiring distance information between the mobile robot and an anchor point, pose information of the mobile robot and environment information through a UWB (ultra wide band), a speedometer and a laser radar; constructing a vertex-constraint graph according to the distance information, the pose information and the environment information; optimizing the vertex-constraint graph through a graph optimization algorithm to obtain optimized track data of the mobile robot; and constructing a grid map through the optimized mobile robot track data and the environment information. The odometer provided by the invention provides accurate pose change of the robot in a short time, integrates UWB positioning information to provide accurate pose change for a long time, and performs map construction by combining data of the laser radar, thereby solving the problem of low precision of the laser radar in constructing an indoor complex environment map.
Description
Technical Field
The invention belongs to the technical field of indoor maps of mobile robots, and particularly relates to a construction method of an indoor map of a mobile robot integrating multiple sensors.
Background
In recent years, mobile robot technology plays an important role in the industrial field, the medical field and the service field, and is well applied to harmful and dangerous occasions such as the national defense field, the space exploration field and the like. In the research field of mobile robots, SLAM has been a popular research topic, and provides a navigation map and a real-time position for a robot, which are the prerequisites for the robot to perform path planning and path tracking, so that it occupies an important position in mobile robot navigation.
Because the laser radar has the advantages of high precision, wide range and high transmission speed, the laser radar is increasingly widely applied to the navigation of the mobile robot, and the construction technology of the indoor environment map based on the laser scanning system is widely applied to the navigation of the robot and is commonly used for robot positioning, construction of the environment map and path planning. The general laser scanner is very expensive, and although many cheap lasers are available on the market at present, the measurement range is limited and the resolution is low. The odometer of the robot can be obtained through a photoelectric encoder, and the error caused by the odometer is larger and larger along with the increase of time, so that the position and posture estimation of the robot has serious deviation. The two-dimensional lidar has a severely reduced mapping accuracy when facing a complex indoor environment.
Disclosure of Invention
Aiming at the defects in the prior art, the method for constructing the indoor map of the mobile robot fusing the multiple sensors solves the problem of low indoor map precision in the prior art.
In order to achieve the purpose of the invention, the invention adopts the technical scheme that: a mobile robot indoor map construction method fusing multiple sensors comprises the following steps:
s1, respectively acquiring distance information between the mobile robot and an anchor point, pose information of the mobile robot and environment information through UWB, a speedometer and a laser radar;
s2, constructing a vertex-constraint graph according to the distance information, the pose information and the environment information;
s3, optimizing the vertex-constraint graph through a graph optimization algorithm to obtain optimized mobile robot track data;
and S4, constructing a grid map through the optimized mobile robot track data and the environment information.
Further, the step S1 includes the following steps:
s1.1, carrying a UWB (ultra wide band) label and a laser radar on a mobile robot, wherein the robot is provided with a speedometer and an encoder;
s1.2, acquiring distance information between the mobile robot and an anchor point through a UWB (ultra wide band) label;
s1.3, acquiring pose data of the robot through a speedometer, wherein the speedometer acquires data through an encoder;
and S1.4, scanning by a laser radar to obtain environment information.
Further, the step S2 includes the following steps:
s2.1, forming a vertex according to the pose information of the mobile robot, and constructing an edge based on the odometer to obtain a first initial image;
s2.2, adding UWB constraint on the first initial graph, and constructing an edge based on UWB to obtain a second initial graph;
and S2.3, performing closed-loop detection through the environment data, adding a laser-based edge on the second initial graph, and constructing a laser-closed-loop edge to obtain a vertex-constraint graph.
Further, the laser-closed loop edge constructing step in step S2.3 is as follows:
a1, constructing a source point cloud set Q ═ { Q ═ Q1,q2,…,qNAnd a target point cloud set P ═ P1,p2,…,pN}；
A2, constructing a rotation matrix R and a translation matrix T of the target point cloud set P, and constructing a target function E (R, T) through the rotation matrix R and the translation matrix T;
a3, setting a threshold, and judging whether E (R, T) is smaller than the threshold, if yes, judging the laser-closed loop is closed, and finishing the construction of the laser-closed loop edge, otherwise, entering the step A4;
a4, substituting the rotation matrix R and the translation matrix T into a source point cloud set Q to obtain a point set M;
a5, substituting the point set M into a target point cloud set P to obtain a new rotation matrix R 'and a new translation matrix T';
a6 assigns R to R ', T to T', substitutes the updated R and T into the objective function E (R, T), and returns to step A3.
Further, the objective function E (R, T) is:
where N represents the total number of points in the target point cloud set, i 1,2iRepresenting the ith point, p, in the source point cloud setiRepresenting the ith point in the target point cloud set.
Further, the specific method for optimizing the vertex-constraint graph by the graph optimization algorithm in step S3 is as follows: and adjusting the pose vertex in the vertex-constraint graph to minimize an error function F (x) of the pose information and obtain the pose vertex which satisfies the constraint to the maximum extent.
Further, the error function f (x) is:
wherein x isiRepresenting pose vertices i, xjRepresenting a set of pose vertices j, C representing a constrained relationship between vertices of the graph，ΩijDenotes xiAnd xjAn information matrix of observed values between, eij(xi,xj,zij) Denotes xiAnd xjSatisfies the constraint zijDegree of (a), zijRepresenting the actual observed value between the pose vertex i and the pose vertex j acquired by the sensor, and the actual observed value zijThe method comprises the steps of pose transformation between adjacent pose vertexes i and j, the distance between the pose vertexes i and anchor points j and the pose transformation between non-adjacent pose vertexes i and j.
Further, the step S4 includes the following sub-steps:
s4.1, dividing the environment into a plurality of grid units according to the environment information;
s4.2, calculating the probability l of each grid unit being occupiedt,ijProbability of being occupied lt,ijA grid cell of 0.8 or more is represented as an obstacle, a grid with an obstacle is represented in gray, and a grid without an obstacle is represented in white, to obtain a grid map.
Further, the probability l that each grid cell is occupiedt,ij：
Wherein lt-1,ijRepresenting the probability that the time grid was occupied at the previous moment, p (m)ij|zt,xt) Representing a grid mijOccupied posterior probability, mijRepresenting a grid cell with abscissa i and ordinate j, ztIs an observed value at time t, xtThe pose of the mobile robot at time t.
The invention has the beneficial effects that:
(1) according to the invention, the distance information between the mobile robot and the anchor point, the pose information of the mobile robot and the environment information are respectively acquired by the UWB, the odometer and the laser radar to construct a map, and various sensors acquire data, so that the constructed map is more accurate.
(2) According to the method, the vertex-constraint graph is constructed, and the vertex-constraint graph is optimized by using the graph optimization algorithm, so that the pose information of the mobile robot is more accurate, and a foundation is laid for constructing an accurate indoor map.
(3) According to the invention, the odometer is used for obtaining the accurate pose change of the robot in a short time, the positioning information of the UWB is fused to obtain the accurate pose change of the robot in a long time, and meanwhile, the accurate map construction is carried out by combining the measurement data of the laser radar, so that the problem of low accuracy of the laser radar in constructing the indoor complex environment map is solved.
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Fig. 1 is a flowchart of an indoor map construction method of a mobile robot with multiple sensors integrated therein according to the present invention.
FIG. 2 is a graph comparing the results of the experiment according to the present invention.
Detailed Description
The following description of the embodiments of the present invention is provided to facilitate the understanding of the present invention by those skilled in the art, but it should be understood that the present invention is not limited to the scope of the embodiments, and it will be apparent to those skilled in the art that various changes may be made without departing from the spirit and scope of the invention as defined and defined in the appended claims, and all matters produced by the invention using the inventive concept are protected.
Embodiments of the present invention will be described in detail below with reference to the accompanying drawings.
As shown in fig. 1, a method for constructing an indoor map of a mobile robot with multiple sensors integrated therein includes the following steps:
s1, respectively acquiring distance information between the mobile robot and an anchor point, pose information of the mobile robot and environment information through UWB, a speedometer and a laser radar;
s2, constructing a vertex-constraint graph according to the distance information, the pose information and the environment information;
s3, optimizing the vertex-constraint graph through a graph optimization algorithm to obtain optimized mobile robot track data;
and S4, constructing a grid map through the optimized mobile robot track data and the environment information.
Step S1 includes the following steps:
s1.1, carrying a UWB (ultra wide band) label and a laser radar on a mobile robot, wherein the robot is provided with a speedometer and an encoder;
s1.2, acquiring distance information between the mobile robot and an anchor point through a UWB (ultra wide band) label;
s1.3, acquiring pose data of the robot through a speedometer, wherein the speedometer acquires data through an encoder;
and S1.4, scanning by a laser radar to obtain environment information.
Step S2 includes the following steps:
s2.1, forming a vertex according to the pose information of the mobile robot, and constructing an edge based on the odometer to obtain a first initial image;
s2.2, adding UWB constraint on the first initial graph, and constructing an edge based on UWB to obtain a second initial graph;
and S2.3, performing closed-loop detection through the environment data, adding a laser-based edge on the second initial graph, and constructing a laser-closed-loop edge to obtain a vertex-constraint graph.
The laser-closed loop edge calculation step in step S2.3 is as follows:
a1, constructing a source point cloud set Q ═ { Q ═ Q1,q2,…,qNAnd a target point cloud set P ═ P1,p2,…,pN}；
A2, constructing a rotation matrix R and a translation matrix T of the target point cloud set P, and constructing a target function E (R, T) through the rotation matrix R and the translation matrix T;
a3, setting a threshold, and judging whether E (R, T) is smaller than the threshold, if yes, judging the laser-closed loop is closed, and finishing the construction of the laser-closed loop edge, otherwise, entering the step A4;
a4, substituting the rotation matrix R and the translation matrix T into a source point cloud set Q to obtain a point set M;
a5, substituting the point set M into a target point cloud set P to obtain a new rotation matrix R 'and a new translation matrix T';
a6 assigns R to R ', T to T', substitutes the updated R and T into the objective function E (R, T), and returns to step A3.
The objective function E (R, T) is:
where N represents the total number of points in the target point cloud set, i 1,2iRepresenting the ith point, p, in the source point cloud setiRepresenting the ith point in the target point cloud set.
The specific method for optimizing the vertex-constraint graph through the graph optimization algorithm in the step S3 is as follows: and adjusting the pose vertex in the vertex-constraint graph to minimize an error function F (x) of the pose information and obtain the pose vertex which satisfies the constraint to the maximum extent.
The error function F (x) is:
wherein x isiRepresenting pose vertices i, xjRepresenting a set of pose vertices j, C representing a constraint relationship between the vertices of the graph, ΩijDenotes xiAnd xjAn information matrix of observed values between, eij(xi,xj,zij) Denotes xiAnd xjSatisfies the constraint zijDegree of (a), zijRepresenting the actual observed value between the pose vertex i and the pose vertex j acquired by the sensor, and the actual observed value zijThe method comprises the steps of pose transformation between adjacent pose vertexes i and j, the distance between the pose vertexes i and anchor points j and the pose transformation between non-adjacent pose vertexes i and j.
Step S4 includes the following substeps:
s4.1, dividing the environment into a plurality of grid units according to the environment information;
s4.2, calculating the probability l of each grid unit being occupiedt,ijProbability of being occupied lt,ijThe grid unit of 0.8 or more is represented as an obstacle, and the grid with the obstacle is usedGray represents, and the grid without obstacles is represented in white to obtain a grid map.
Probability l that each grid cell is occupiedt,ij：
Wherein lt-1,ijRepresenting the probability that the time grid was occupied at the previous moment, p (m)ij|zt,xt) Representing a grid mijOccupied posterior probability, mijRepresenting a grid cell with abscissa i and ordinate j, ztIs an observed value at the time t, wherein the observed value represents the distance and the angle between the mobile robot and the obstacle measured by the laser radar, xtThe pose of the mobile robot at time t.
In this embodiment, the experimental scenario is selected as a corridor with two opposite sides as walls.
As shown in fig. 2, a is a map constructed by an original odometer, b is a map constructed by the odometer and the UWB together, and c is a map constructed by the UWB, the odometer and the laser radar according to the present invention; through comparative analysis of experimental results, the construction of the wall body by the map constructed by the method is more accurate, and the obstacles are effectively identified.
According to the invention, the distance information between the mobile robot and the anchor point, the pose information of the mobile robot and the environment information are respectively acquired by the UWB, the odometer and the laser radar to construct a map, and various sensors acquire data, so that the constructed map is more accurate.
According to the method, the vertex-constraint graph is constructed, and the vertex-constraint graph is optimized by using the graph optimization algorithm, so that the pose information of the mobile robot is more accurate, and a foundation is laid for constructing an accurate indoor map.
According to the invention, the odometer is used for obtaining the accurate pose change of the robot in a short time, the positioning information of the UWB is fused to obtain the accurate pose change of the robot in a long time, and meanwhile, the accurate map construction is carried out by combining the measurement data of the laser radar, so that the problem of low accuracy of the laser radar in constructing the indoor complex environment map is solved.
Claims (9)
1. A mobile robot indoor map construction method fused with multiple sensors is characterized by comprising the following steps:
s1, respectively acquiring distance information between the mobile robot and an anchor point, pose information of the mobile robot and environment information through UWB, a speedometer and a laser radar;
s2, constructing a vertex-constraint graph according to the distance information, the pose information and the environment information;
s3, optimizing the vertex-constraint graph through a graph optimization algorithm to obtain optimized mobile robot track data;
and S4, constructing a grid map through the optimized mobile robot track data and the environment information.
2. The multi-sensor-integrated mobile robot indoor mapping method of claim 1, wherein the step S1 comprises the steps of:
s1.1, carrying a UWB (ultra wide band) label and a laser radar on a mobile robot, wherein the robot is provided with a speedometer and an encoder;
s1.2, acquiring distance information between the mobile robot and an anchor point through a UWB (ultra wide band) label;
s1.3, acquiring pose data of the robot through a speedometer, wherein the speedometer acquires data through an encoder;
and S1.4, scanning by a laser radar to obtain environment information.
3. The multi-sensor-integrated mobile robot indoor mapping method of claim 1, wherein the step S2 comprises the steps of:
s2.1, forming a vertex according to the pose information of the mobile robot, and constructing an edge based on the odometer to obtain a first initial image;
s2.2, adding UWB constraint on the first initial graph, and constructing an edge based on UWB to obtain a second initial graph;
and S2.3, performing closed-loop detection through the environment data, adding a laser-based edge on the second initial graph, and constructing a laser-closed-loop edge to obtain a vertex-constraint graph.
4. The method for constructing the indoor map of the mobile robot fusing the multiple sensors as claimed in claim 3, wherein the step S2.3 of constructing the laser-closed loop edge comprises the following steps:
a1, constructing a source point cloud set Q ═ { Q ═ Q1,q2,…,qNAnd a target point cloud set P ═ P1,p2,…,pN}；
A2, constructing a rotation matrix R and a translation matrix T of the target point cloud set P, and constructing a target function E (R, T) through the rotation matrix R and the translation matrix T;
a3, setting a threshold, and judging whether E (R, T) is smaller than the threshold, if yes, judging the laser-closed loop is closed, and finishing the construction of the laser-closed loop edge, otherwise, entering the step A4;
a4, substituting the rotation matrix R and the translation matrix T into a source point cloud set Q to obtain a point set M;
a5, substituting the point set M into a target point cloud set P to obtain a new rotation matrix R 'and a new translation matrix T';
a6 assigns R to R ', T to T', substitutes the updated R and T into the objective function E (R, T), and returns to step A3.
5. The multi-sensor-fused mobile robot indoor mapping method according to claim 4, wherein the objective function E (R, T) is:
where N represents the total number of points in the target point cloud set, i 1,2iRepresenting the ith point, p, in the source point cloud setiRepresenting the ith point in the target point cloud set.
6. The method for constructing the indoor map of the mobile robot with the fusion of the multiple sensors as claimed in claim 1, wherein the specific method for optimizing the vertex-constraint map by the map optimization algorithm in the step S3 is as follows: and adjusting the pose vertex in the vertex-constraint graph to minimize an error function F (x) of the pose information and obtain the pose vertex which satisfies the constraint to the maximum extent.
7. The method for constructing the indoor map of the mobile robot with the fusion of the multiple sensors as claimed in claim 6, wherein the error function F (x) is:
wherein x isiRepresenting pose vertices i, xjRepresenting a set of pose vertices j, C representing a constraint relationship between the vertices of the graph, ΩijDenotes xiAnd xjAn information matrix of observed values between, eij(xi,xj,zij) Denotes xiAnd xjSatisfies the constraint zijDegree matrix of (z)ijRepresenting the actual observed value between the pose vertex i and the pose vertex j acquired by the sensor, and the actual observed value zijThe method comprises the steps of pose transformation between adjacent pose vertexes i and j, the distance between the pose vertexes i and anchor points j and the pose transformation between non-adjacent pose vertexes i and j.
8. The method for constructing an indoor map of a mobile robot with multiple sensors integrated therein according to claim 1, wherein the step S4 comprises the following substeps:
s4.1, dividing the environment into a plurality of grid units according to the environment information;
s4.2, calculating the probability l of each grid unit being occupiedt,ijProbability of being occupied lt,ijA grid cell of 0.8 or more is represented as an obstacle, a grid with an obstacle is represented in gray, and a grid without an obstacle is represented in white, to obtain a grid map.
9. The multi-sensor-fused mobile robot indoor mapping method of claim 8, wherein the probability/, that each grid cell is occupiedt,ijComprises the following steps:
wherein lt-1,ijRepresenting the probability that the time grid was occupied at the previous moment, p (m)ij|zt,xt) Representing a grid mijOccupied posterior probability, mijRepresenting a grid cell with abscissa i and ordinate j, ztIs an observed value at time t, xtThe pose of the mobile robot at time t.
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|---|---|---|---|---|
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Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20070293985A1 (en) * | 2006-06-20 | 2007-12-20 | Samsung Electronics Co., Ltd. | Method, apparatus, and medium for building grid map in mobile robot and method, apparatus, and medium for cell decomposition that uses grid map |
| CN106643720A (en) * | 2016-09-28 | 2017-05-10 | 深圳市普渡科技有限公司 | Method for map construction based on UWB indoor locating technology and laser radar |
| CN107179080A (en) * | 2017-06-07 | 2017-09-19 | 纳恩博（北京）科技有限公司 | The localization method and device of electronic equipment, electronic equipment, electronic positioning system |
| CN109059942A (en) * | 2018-08-22 | 2018-12-21 | 中国矿业大学 | A kind of high-precision underground navigation map building system and construction method |
| CN109848996A (en) * | 2019-03-19 | 2019-06-07 | 西安交通大学 | Extensive three-dimensional environment map creating method based on figure optimum theory |
| CN109974712A (en) * | 2019-04-22 | 2019-07-05 | 广东亿嘉和科技有限公司 | It is a kind of that drawing method is built based on the Intelligent Mobile Robot for scheming optimization |
-
2019
- 2019-09-26 CN CN201910915091.4A patent/CN110645974B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20070293985A1 (en) * | 2006-06-20 | 2007-12-20 | Samsung Electronics Co., Ltd. | Method, apparatus, and medium for building grid map in mobile robot and method, apparatus, and medium for cell decomposition that uses grid map |
| CN106643720A (en) * | 2016-09-28 | 2017-05-10 | 深圳市普渡科技有限公司 | Method for map construction based on UWB indoor locating technology and laser radar |
| CN107179080A (en) * | 2017-06-07 | 2017-09-19 | 纳恩博（北京）科技有限公司 | The localization method and device of electronic equipment, electronic equipment, electronic positioning system |
| CN109059942A (en) * | 2018-08-22 | 2018-12-21 | 中国矿业大学 | A kind of high-precision underground navigation map building system and construction method |
| CN109848996A (en) * | 2019-03-19 | 2019-06-07 | 西安交通大学 | Extensive three-dimensional environment map creating method based on figure optimum theory |
| CN109974712A (en) * | 2019-04-22 | 2019-07-05 | 广东亿嘉和科技有限公司 | It is a kind of that drawing method is built based on the Intelligent Mobile Robot for scheming optimization |
Non-Patent Citations (1)
| Title |
|---|
| JENS-STEFFEN GUTMANN ET AL.: "Incremental Mapping of Large Cyclic Environments", 《PROC THE CONFERENCE ON INTELLIGENT ROBOTS & APPLICATIONS》 * |
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