CN114383622B - Robot positioning method, robot, and computer-readable storage medium - Google Patents
Robot positioning method, robot, and computer-readable storage medium Download PDFInfo
- Publication number
- CN114383622B CN114383622B CN202111615155.2A CN202111615155A CN114383622B CN 114383622 B CN114383622 B CN 114383622B CN 202111615155 A CN202111615155 A CN 202111615155A CN 114383622 B CN114383622 B CN 114383622B
- Authority
- CN
- China
- Prior art keywords
- current
- subgraph
- robot
- region subgraph
- region
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000000034 method Methods 0.000 title claims abstract description 55
- 230000004044 response Effects 0.000 claims description 3
- 238000005516 engineering process Methods 0.000 description 9
- 238000010586 diagram Methods 0.000 description 3
- 238000007405 data analysis Methods 0.000 description 2
- 230000036544 posture Effects 0.000 description 2
- 238000012216 screening Methods 0.000 description 2
- 238000004458 analytical method Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000004364 calculation method Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 230000007613 environmental effect Effects 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 238000005457 optimization Methods 0.000 description 1
Classifications
-
- 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
- G01C21/26—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 specially adapted for navigation in a road network
- G01C21/28—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 specially adapted for navigation in a road network with correlation of data from several navigational instruments
- G01C21/30—Map- or contour-matching
Landscapes
- Engineering & Computer Science (AREA)
- Radar, Positioning & Navigation (AREA)
- Remote Sensing (AREA)
- Automation & Control Theory (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Manipulator (AREA)
Abstract
The application discloses a robot positioning method, a robot and a computer readable storage medium, wherein the robot positioning method comprises the following steps: constructing a current region subgraph; matching the current region subgraph with each region subgraph in a pre-stored region subgraph set respectively to determine at least one region subgraph to be determined; acquiring a first grid set corresponding to a current region subgraph and at least one second grid set corresponding to at least one region subgraph to be determined respectively, and acquiring a target region subgraph based on the first grid set and the at least one second grid set; based on the target region subgraph, a current location is determined. Based on the mode, the positioning speed of the robot is improved.
Description
Technical Field
The present application relates to the field of positioning technologies, and in particular, to a robot positioning method, a robot, and a computer readable storage medium.
Background
In the prior art, when positioning a robot in an environment, it is generally required to match sensor data (such as laser radar data) collected at a current position of the robot with pre-stored global map data, so as to finally determine the current position of the robot.
The defect of the prior art is that when the global map corresponding to the environment where the robot is located is large, the sensor data acquired at the current position of the robot is matched with the pre-stored global map data, so that excessive time is consumed, and the positioning speed of the robot is slower.
Disclosure of Invention
The application mainly solves the technical problem of how to improve the positioning speed of the robot.
In order to solve the technical problems, a first technical scheme adopted by the application is as follows: a robot positioning method, comprising: constructing a current region subgraph, wherein the current region subgraph is a region subgraph corresponding to the current position of the robot, and the region subgraph is a probability grid map; matching the current region subgraph with each region subgraph in a pre-stored region subgraph set respectively to determine at least one region subgraph to be determined; acquiring a first grid set corresponding to a current region subgraph and at least one second grid set corresponding to at least one region subgraph to be determined respectively, and acquiring a target region subgraph based on the first grid set and the at least one second grid set; based on the target region subgraph, a current location is determined.
In order to solve the technical problems, a second technical scheme adopted by the application is as follows: a robot, comprising: a memory and a processor; the memory is used for storing program instructions and the processor is used for executing the program instructions to realize the method.
In order to solve the technical problems, a third technical scheme adopted by the application is as follows: a computer readable storage medium storing program instructions which, when executed by a processor, implement the above-described method.
The application has the beneficial effects that: compared with the prior art, the method comprises the steps of constructing the current region subgraph corresponding to the current position of the robot, respectively matching the current region subgraph with each graph in the pre-stored region subgraph set to determine at least one to-be-determined region subgraph, and then determining the target region subgraph in the at least one to-be-determined region subgraph according to the first grid set and the at least one second grid set to determine the current position of the robot based on the target region subgraph. Based on the above mode, in the step of matching the current region subgraph with each graph in the pre-stored region subgraph set respectively, the matching processing of different object combinations can be performed simultaneously based on the multithreading technology, so that the time consumed by the matching processing is reduced, and the positioning speed of the robot can be further improved.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly described below, and it is apparent that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of a first embodiment of the robotic positioning method of the present application;
FIG. 2 is a flow chart of a second embodiment of the robotic positioning method of the present application;
FIG. 3 is a flow chart of a third embodiment of the robotic positioning method of the present application;
FIG. 4 is a flow chart of a fourth embodiment of the robotic positioning method of the present application;
FIG. 5 is a schematic view of a robot according to an embodiment of the present application;
FIG. 6 is a schematic diagram of an embodiment of a computer readable storage medium of the present application.
Detailed Description
The following description of the embodiments of the present application will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present application, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the present application without making any inventive effort, are intended to fall within the scope of the present application.
The terms "first" and "second" in the present application are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. In the description of the present application, "multiple" means at least two, such as two, three, etc., unless specifically defined otherwise. Furthermore, the terms "comprise" and "have," as well as any variations thereof, are intended to cover a non-exclusive inclusion. For example, a process, method, system, article, or apparatus that comprises a list of steps or elements is not limited to only those listed steps or elements but may include other steps or elements not listed or inherent to such process, method, article, or apparatus.
The application first discloses a robot positioning method, as shown in fig. 1, fig. 1 is a flow chart of a first embodiment of the robot positioning method of the application, and the robot positioning method can comprise:
step S11: and constructing a current region subgraph.
The current region subgraph is a region subgraph corresponding to the current position of the robot, and the region subgraph is a probability grid map.
The robot can be controlled to move (such as to rotate in place or to and fro for a distance) in the area corresponding to the current position, and corresponding environmental data are collected based on the sensors on the robot when the robot moves to different places or different postures so as to form a plurality of grids corresponding to the different places or different postures in the area. Based on a plurality of grids, a probability grid map corresponding to the current position of the robot can be constructed and generated to serve as a current region subgraph of the robot.
The sensors of the robot may include at least one of 2D lidar, 3D lidar, cameras, 3D point cloud sensors (e.g., kinect, zenized, binocular cameras, etc.), and other sensors, without limitation.
Step S12: and respectively matching the current region subgraph with each region subgraph in a pre-stored region subgraph set to determine at least one region subgraph to be determined.
The pre-stored regional subgraph set can be understood as global map data corresponding to the current environment, and the pre-stored regional subgraph can comprise a plurality of regional subgraphs formed by dividing a global map.
And carrying out one-to-one matching analysis on the current region subgraph and each region subgraph in the pre-stored region subgraph set respectively to determine at least one to-be-determined region subgraph matched with the current region subgraph in all the region subgraphs in the pre-stored region subgraph set.
Step S13: acquiring a first grid set corresponding to the current region subgraph and at least one second grid set corresponding to at least one region subgraph to be determined respectively, and acquiring a target region subgraph based on the first grid set and the at least one second grid set.
The current region subgraph is a region subgraph constructed based on all grids in the first grid set, and the region subgraph to be determined is a region subgraph constructed based on all grids in the corresponding second grid set.
And respectively carrying out data analysis on the first grid set and at least one second grid set to determine a pending area subgraph which is most similar to the current area subgraph, and recording the pending area subgraph as a target area subgraph.
Step S14: based on the target region subgraph, a current location is determined.
The position of the current region subgraph on the global map can be determined based on the target region subgraph, so that the current position of the robot can be determined.
Compared with the prior art, the method comprises the steps of constructing the current region subgraph corresponding to the current position of the robot, respectively matching the current region subgraph with each graph in the pre-stored region subgraph set to determine at least one to-be-determined region subgraph, and then determining the target region subgraph in the at least one to-be-determined region subgraph according to the first grid set and the at least one second grid set to determine the current position of the robot based on the target region subgraph. Based on the above mode, in the step of matching the current region subgraph with each graph in the pre-stored region subgraph set respectively, the matching processing of different object combinations can be performed simultaneously based on the multithreading technology, so that the time consumed by the matching processing is reduced, and the positioning speed of the robot can be further improved.
Optionally, step S14 may specifically include:
based on the target region subgraph, a current position and a current pose of the robot are determined.
Specifically, based on the target region subgraph, the position of the current region subgraph on the global map may be determined, so as to determine the current pose of the robot on the global map, for example: the orientation of the robot on the global map. Based on the mode, the reliability of the robot positioning method can be effectively improved.
The present application also proposes a robot positioning method, as shown in fig. 2, fig. 2 is a schematic flow chart of a second embodiment of the robot positioning method of the present application, and steps S11, S12 and S14 in the second embodiment are the same as those in the first embodiment, and are not repeated herein.
The robot positioning method comprises the following steps:
step S11: and constructing a current region subgraph.
Step S12: and respectively matching the current region subgraph with each region subgraph in a pre-stored region subgraph set to determine at least one region subgraph to be determined.
Step S131: and acquiring a first grid set corresponding to the current region subgraph and at least one second grid set corresponding to at least one region subgraph to be determined respectively.
The current region subgraph is a region subgraph constructed based on all grids in the first grid set, and the region subgraph to be determined is a region subgraph constructed based on all grids in the corresponding second grid set.
Step S132: and obtaining the similarity between each undetermined region sub-graph and the current region sub-graph based on the first grid set and at least one second grid set.
The first grid set and at least one second grid set can be subjected to data analysis respectively to calculate and obtain the similarity between the current region subgraph and each undetermined region subgraph.
Step S133: and taking one region subgraph of the preset number of the undetermined region subgraphs with the highest similarity as a target region subgraph.
The obtained all the similarities can be sequenced according to the sequence from big to small, the preset number of the similarities at the forefront of the sequence is screened out, and a region subgraph is determined as a target region subgraph from the preset number of the undetermined region subgraphs corresponding to the screened similarities.
Step S14: based on the target region subgraph, a current location is determined.
Specifically, based on the mode, a target region subgraph which is similar to the current region subgraph sufficiently can be determined from a pre-stored region subgraph set, and the current position of the robot is determined based on the target region subgraph, so that the accuracy of the robot positioning method is improved.
Optionally, the occupancy probabilities of the gratings in the first set of gratings and the second set of gratings are probabilities of the gratings corresponding to the obstacle.
Step S132 may specifically include:
And determining grids with the occupation probability larger than the first probability and the occupation probability larger than the second probability in the corresponding projection grids of the target second grid set in the first grid set as accumulated grids, wherein the target second grid set is one set in at least one second grid set.
And determining the sum of the occupation probabilities of all the accumulated grids as the similarity between the sub-image of the undetermined area corresponding to the target second grid set and the sub-image of the current area.
Specifically, the region subgraph is a probability grid map.
An area subgraph may be generated based on all of the gratings in a set of gratings and the occupancy probabilities corresponding to each of the gratings, where one grating contains sensor data collected by the robot at one location, and the occupancy probabilities may represent probabilities that the corresponding grating corresponds to sensor data where the robot collides with an obstacle.
The calculation formula of the occupancy probability is as follows:
wherein p is the occupancy probability of the target grating, j is the total number of times the robot collects sensor data corresponding to the target grating, i is the number of times the robot determines that the target grating corresponds to the sensor data of the robot colliding with an obstacle, the target grating is one grating in any grating set, and the sensor data can comprise at least one of laser radar data and other types of sensor data, which is not limited herein.
When determining the similarity between the sub-image of the undetermined area corresponding to the second grid set and the sub-image of the current area, and determining the sum of the occupation probabilities of all accumulated grids as the similarity between the sub-image of the undetermined area corresponding to the second grid set and the sub-image of the current area, the first probability and the second probability may be 1/2, and the specific flow may be as follows:
and mutually determining grids corresponding to the same place in the first grid set in the second grid set as projection grids.
And screening grids with the occupation probability larger than 1/2 from the first grid set, and recording the grids as grids to be accumulated.
Determining projection grids of all grids to be accumulated in the second grid set, screening grids with the occupation probability larger than 1/2 of the corresponding projection grids from all grids to be accumulated, and recording the grids as accumulated grids.
And accumulating and summing the occupancy probabilities corresponding to all accumulated grids to obtain the similarity between the sub-graph of the undetermined area corresponding to the second grid set of the target and the sub-graph of the current area.
The specific flow can be executed for each sub-graph of the undetermined area for a plurality of times to obtain the similarity corresponding to all sub-graphs of the undetermined area.
And finally, determining one region subgraph from the preset number of the undetermined region subgraphs with the highest similarity as a target region subgraph, wherein the preset number can be 1,3 or other numbers, and the target region subgraph is specifically determined according to actual requirements and is not limited herein.
Based on the mode, the accuracy of the similarity between the acquired current region subgraph and each undetermined region subgraph can be improved, and the accuracy of the robot positioning method is further improved.
When the specific flow is executed for multiple times for each sub-graph of the undetermined area to obtain the similarity corresponding to all sub-graphs of the undetermined area, the multi-thread technology can be simultaneously executed, so that the time consumed by the matching process is reduced, and the positioning speed of the robot can be further improved.
Compared with the prior art, the method comprises the steps of constructing the current region subgraph corresponding to the current position of the robot, respectively matching the current region subgraph with each graph in the pre-stored region subgraph set to determine at least one to-be-determined region subgraph, and then determining the target region subgraph in the at least one to-be-determined region subgraph according to the first grid set and the at least one second grid set to determine the current position of the robot based on the target region subgraph. Based on the above mode, in the step of matching the current region subgraph with each graph in the pre-stored region subgraph set respectively, the matching processing of different object combinations can be performed simultaneously based on the multithreading technology, so that the time consumed by the matching processing is reduced, and the positioning speed of the robot can be further improved.
The present application also proposes a robot positioning method, as shown in fig. 3, fig. 3 is a schematic flow chart of a third embodiment of the robot positioning method of the present application, and steps S11, S13 and S14 in the third embodiment are the same as those in the first embodiment, and are not repeated herein.
The robot positioning method comprises the following steps:
step S11: and constructing a current region subgraph.
Step S121: and respectively matching the current region subgraph with each region subgraph in a pre-stored region subgraph set based on a preset algorithm to determine at least one region subgraph to be determined.
The preset algorithm comprises at least one of a correlation scanning matching algorithm, an iterative nearest point algorithm and a least square algorithm.
Step S13: acquiring a first grid set corresponding to the current region subgraph and at least one second grid set corresponding to at least one region subgraph to be determined respectively, and acquiring a target region subgraph based on the first grid set and the at least one second grid set.
Step S14: based on the target region subgraph, a current location is determined.
Specifically, the position relation between the current region subgraph and each region subgraph in the pre-stored region subgraph set can be determined through at least one of a correlation scanning matching algorithm of violent searching, an iterative closest point algorithm based on point cloud matching, a least square algorithm based on optimization and other algorithms, so that whether the current region subgraph is matched with each region subgraph in the pre-stored region subgraph set or not is determined.
Based on the mode, the matching accuracy can be improved, and the accuracy of the robot positioning method is further improved.
Compared with the prior art, the method comprises the steps of constructing the current region subgraph corresponding to the current position of the robot, respectively matching the current region subgraph with each graph in the pre-stored region subgraph set to determine at least one to-be-determined region subgraph, and then determining the target region subgraph in the at least one to-be-determined region subgraph according to the first grid set and the at least one second grid set to determine the current position of the robot based on the target region subgraph. Based on the above mode, in the step of matching the current region subgraph with each graph in the pre-stored region subgraph set respectively, the matching processing of different object combinations can be performed simultaneously based on the multithreading technology, so that the time consumed by the matching processing is reduced, and the positioning speed of the robot can be further improved.
The present application also proposes a robot positioning method, as shown in fig. 4, fig. 4 is a flow chart of a fourth embodiment of the robot positioning method according to the present application, and the robot positioning method in the fourth embodiment includes steps S11-S14 in the first embodiment, which are not repeated herein.
Before step S11, the robot positioning method further includes:
Step S21: sensor data is recorded every first distance moved within the current area based on a preset movement route.
The current area range is a range with a distance from a current starting point within a second distance, and the current starting point is a movement starting point of the robot within the current area range. The preset movement route may be an arcuate route that traverses the entire current area, may be a pivot, may traverse a distance, or may be other routes, which are not limited herein.
Step S22: and constructing a region subgraph corresponding to the current region range based on the sensor data corresponding to the current region range to store in a pre-stored region subgraph set in response to the distance between the robot and the current starting point being greater than a second distance.
Specifically, based on the mode, the region subgraph with perfect information corresponding to one region can be constructed, and then the accuracy of subsequent robot positioning based on the pre-stored region subgraph set can be improved.
Optionally, as shown in fig. 4, after step S22, the robot positioning method further includes:
Step S23: it is determined whether to end the composition.
If the determination result in step S23 is no, step S24 is executed, and then step S21 and subsequent steps are executed. If the determination result in step S23 is yes, step S25 is executed.
Step S24: the current position of the robot is determined as a new current starting point.
Step S25: the composition is ended.
Specifically, whether to end the composition may be determined according to whether all the region subgraphs in the global map have been constructed currently, if all the region subgraphs in the global map have been constructed, the determination result is yes, and if all the region subgraphs in the global map have not been constructed, the determination result is no.
Based on the mode, the phenomenon of missing the constructed region subgraph can be avoided, and the accuracy of subsequent robot positioning based on the pre-stored region subgraph set is improved.
Optionally, the sensor data is lidar data.
Compared with the prior art, the method comprises the steps of constructing the current region subgraph corresponding to the current position of the robot, respectively matching the current region subgraph with each graph in the pre-stored region subgraph set to determine at least one to-be-determined region subgraph, and then determining the target region subgraph in the at least one to-be-determined region subgraph according to the first grid set and the at least one second grid set to determine the current position of the robot based on the target region subgraph. Based on the above mode, in the step of matching the current region subgraph with each graph in the pre-stored region subgraph set respectively, the matching processing of different object combinations can be performed simultaneously based on the multithreading technology, so that the time consumed by the matching processing is reduced, and the positioning speed of the robot can be further improved.
The present application also proposes a robot, as shown in fig. 5, fig. 5 is a schematic structural diagram of an embodiment of the robot of the present application, and the robot 50 includes: a processor 51, a memory 52 and a bus 53.
The processor 51 and the memory 52 are respectively connected to the bus 53, and the memory 52 stores program instructions, and the processor 51 is configured to execute the program instructions to implement the robot positioning method in the above embodiment.
In this embodiment, the processor 51 may also be referred to as a CPU (Central Processing Unit ). The processor 51 may be an integrated circuit chip with signal processing capabilities. Processor 51 may also be a general purpose processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other programmable logic device, discrete gate or transistor logic, discrete hardware components. The general purpose processor may be a microprocessor or the processor 51 may be any conventional processor or the like.
Compared with the prior art, the method comprises the steps of constructing the current region subgraph corresponding to the current position of the robot, respectively matching the current region subgraph with each graph in the pre-stored region subgraph set to determine at least one to-be-determined region subgraph, and then determining the target region subgraph in the at least one to-be-determined region subgraph according to the first grid set and the at least one second grid set to determine the current position of the robot based on the target region subgraph. Based on the above mode, in the step of matching the current region subgraph with each graph in the pre-stored region subgraph set respectively, the matching processing of different object combinations can be performed simultaneously based on the multithreading technology, so that the time consumed by the matching processing is reduced, and the positioning speed of the robot can be further improved.
The present application also proposes a computer readable storage medium, as shown in fig. 6, fig. 6 is a schematic structural diagram of an embodiment of the computer readable storage medium of the present application, where the computer readable storage medium 60 has program instructions 61 stored thereon, and the program instructions 61 when executed by a processor (not shown) implement the robot positioning method in the above embodiment.
The computer readable storage medium 60 of the present embodiment may be, but is not limited to, a usb disk, an SD card, a PD optical drive, a mobile hard disk, a high capacity floppy drive, a flash memory, a multimedia memory card, a server, a storage unit in an FPGA or an ASIC, and the like.
Compared with the prior art, the method comprises the steps of constructing the current region subgraph corresponding to the current position of the robot, respectively matching the current region subgraph with each graph in the pre-stored region subgraph set to determine at least one to-be-determined region subgraph, and then determining the target region subgraph in the at least one to-be-determined region subgraph according to the first grid set and the at least one second grid set to determine the current position of the robot based on the target region subgraph. Based on the above mode, in the step of matching the current region subgraph with each graph in the pre-stored region subgraph set respectively, the matching processing of different object combinations can be performed simultaneously based on the multithreading technology, so that the time consumed by the matching processing is reduced, and the positioning speed of the robot can be further improved.
The foregoing description is only of embodiments of the present application, and is not intended to limit the scope of the application, and all equivalent structures or equivalent processes using the descriptions and the drawings of the present application or directly or indirectly applied to other related technical fields are included in the scope of the present application.
Claims (8)
1. A robot positioning method, comprising:
Constructing a current region subgraph, wherein the current region subgraph is a region subgraph corresponding to the current position of the robot, and the region subgraph is a probability grid map;
Matching the current region subgraph with each region subgraph in a pre-stored region subgraph set respectively to determine at least one region subgraph to be determined;
Acquiring a first grid set corresponding to the current region subgraph and at least one second grid set corresponding to the at least one region subgraph to be determined respectively, and acquiring a target region subgraph based on the first grid set and the at least one second grid set;
determining the current position based on the target region subgraph;
The step of obtaining a target area subgraph based on the first grid set and the at least one second grid set comprises the following steps:
Determining grids in the first grid set, wherein the occupancy probability of the grids is larger than the first probability, and the occupancy probability of corresponding projection grids of a target second grid set is larger than the second probability, as accumulated grids, wherein the target second grid set is one set in the at least one second grid set;
Determining the sum of the occupation probabilities of all the accumulated grids as the similarity between the undetermined region subgraph corresponding to the target second grid set and the current region subgraph;
Taking one region subgraph of the preset number of the undetermined region subgraphs with highest similarity as the target region subgraph;
the occupancy probabilities of the grids in the first grid set and the second grid set are probabilities that the corresponding grids correspond to sensor data of the robot colliding with an obstacle.
2. The robot positioning method according to claim 1, wherein the step of matching the current region subgraph with each region subgraph in a set of pre-stored region subgraphs to determine at least one region subgraph to be determined comprises:
And respectively matching the current region subgraph with each region subgraph in a pre-stored region subgraph set based on a preset algorithm to determine at least one region subgraph to be determined, wherein the preset algorithm comprises at least one of a correlation scanning matching algorithm, an iterative closest point algorithm and a least square algorithm.
3. The robotic positioning method of claim 1, wherein prior to the step of constructing the current region sub-map, the robotic positioning method further comprises:
Based on a preset movement route, recording sensor data once every first distance is moved in a current area range, wherein the current area range is a range with a distance within a second distance from a current starting point, and the current starting point is a movement starting point of the robot in the current area range;
and constructing a region subgraph corresponding to the current region range based on sensor data corresponding to the current region range in response to the distance between the robot and the current starting point being greater than the second distance, so as to store the pre-stored region subgraph set.
4. The robot positioning method according to claim 3, wherein after the step of constructing a region sub-map corresponding to the current region range based on sensor data corresponding to the current region range to store in the pre-stored region sub-map set in response to the distance of the robot from the current starting point being greater than the second distance, the robot positioning method further comprises:
judging whether to finish composition;
If not, determining the current position of the robot as a new current starting point, and returning to execute the step and the subsequent steps of recording the sensor data once every time the first distance is moved in the current area range based on the preset movement route.
5. A method of positioning a robot as claimed in claim 3, wherein the sensor data is lidar data.
6. The robot positioning method of claim 1, wherein the step of determining the current position based on the target area subgraph comprises:
And determining the current position and the current gesture of the robot based on the target region subgraph.
7. A robot, comprising: a memory and a processor;
The memory is configured to store program instructions and the processor is configured to execute the program instructions to implement the method of any one of claims 1 to 6.
8. A computer readable storage medium storing program instructions which, when executed by a processor, implement the method of any one of claims 1 to 6.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111615155.2A CN114383622B (en) | 2021-12-27 | 2021-12-27 | Robot positioning method, robot, and computer-readable storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111615155.2A CN114383622B (en) | 2021-12-27 | 2021-12-27 | Robot positioning method, robot, and computer-readable storage medium |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN114383622A CN114383622A (en) | 2022-04-22 |
| CN114383622B true CN114383622B (en) | 2024-04-19 |
Family
ID=81197764
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202111615155.2A Active CN114383622B (en) | 2021-12-27 | 2021-12-27 | Robot positioning method, robot, and computer-readable storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114383622B (en) |
Families Citing this family (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN118644556B (en) * | 2024-08-15 | 2024-10-18 | 天目山实验室 | Unmanned aerial vehicle positioning method and device based on image feature matching |
Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107037806A (en) * | 2016-02-04 | 2017-08-11 | 科沃斯机器人股份有限公司 | Self-movement robot re-positioning method and the self-movement robot using this method |
| WO2019126332A1 (en) * | 2017-12-19 | 2019-06-27 | Carnegie Mellon University | Intelligent cleaning robot |
| CN110207710A (en) * | 2019-06-26 | 2019-09-06 | 北京小狗智能机器人技术有限公司 | Robot method for relocating and device |
| CN110675457A (en) * | 2019-09-27 | 2020-01-10 | Oppo广东移动通信有限公司 | Positioning method and device, equipment and storage medium |
| CN111830985A (en) * | 2020-07-24 | 2020-10-27 | 中南大学 | Multi-robot positioning method, system and centralized communication system |
| CN112102151A (en) * | 2020-07-27 | 2020-12-18 | 广州视源电子科技股份有限公司 | Grid map generation method and device, mobile intelligent device and storage medium |
| CN112802103A (en) * | 2021-02-01 | 2021-05-14 | 深圳万拓科技创新有限公司 | Pose repositioning method, device, equipment and medium of laser sweeper |
| CN113011517A (en) * | 2021-03-30 | 2021-06-22 | 上海商汤临港智能科技有限公司 | Positioning result detection method and device, electronic equipment and storage medium |
| CN113448326A (en) * | 2020-03-25 | 2021-09-28 | 北京京东乾石科技有限公司 | Robot positioning method and device, computer storage medium and electronic equipment |
| CN113763551A (en) * | 2021-09-08 | 2021-12-07 | 北京易航远智科技有限公司 | Point cloud-based rapid repositioning method for large-scale mapping scene |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR20210042537A (en) * | 2019-10-10 | 2021-04-20 | 엘지전자 주식회사 | Method of estimating position in local area in large sapce and robot and cloud server implementing thereof |
-
2021
- 2021-12-27 CN CN202111615155.2A patent/CN114383622B/en active Active
Patent Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107037806A (en) * | 2016-02-04 | 2017-08-11 | 科沃斯机器人股份有限公司 | Self-movement robot re-positioning method and the self-movement robot using this method |
| WO2019126332A1 (en) * | 2017-12-19 | 2019-06-27 | Carnegie Mellon University | Intelligent cleaning robot |
| CN110207710A (en) * | 2019-06-26 | 2019-09-06 | 北京小狗智能机器人技术有限公司 | Robot method for relocating and device |
| CN110675457A (en) * | 2019-09-27 | 2020-01-10 | Oppo广东移动通信有限公司 | Positioning method and device, equipment and storage medium |
| CN113448326A (en) * | 2020-03-25 | 2021-09-28 | 北京京东乾石科技有限公司 | Robot positioning method and device, computer storage medium and electronic equipment |
| CN111830985A (en) * | 2020-07-24 | 2020-10-27 | 中南大学 | Multi-robot positioning method, system and centralized communication system |
| CN112102151A (en) * | 2020-07-27 | 2020-12-18 | 广州视源电子科技股份有限公司 | Grid map generation method and device, mobile intelligent device and storage medium |
| CN112802103A (en) * | 2021-02-01 | 2021-05-14 | 深圳万拓科技创新有限公司 | Pose repositioning method, device, equipment and medium of laser sweeper |
| CN113011517A (en) * | 2021-03-30 | 2021-06-22 | 上海商汤临港智能科技有限公司 | Positioning result detection method and device, electronic equipment and storage medium |
| CN113763551A (en) * | 2021-09-08 | 2021-12-07 | 北京易航远智科技有限公司 | Point cloud-based rapid repositioning method for large-scale mapping scene |
Also Published As
| Publication number | Publication date |
|---|---|
| CN114383622A (en) | 2022-04-22 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| JP7167397B2 (en) | Method and apparatus for processing point cloud data | |
| CN108319655B (en) | Method and device for generating grid map | |
| US11086016B2 (en) | Method and apparatus for tracking obstacle | |
| CN109521757B (en) | Static obstacle identification method and device | |
| US11537138B2 (en) | Adaptive region division method and system | |
| US9058538B1 (en) | Bundle adjustment based on image capture intervals | |
| CN111680673B (en) | Method, device and equipment for detecting dynamic object in grid map | |
| US20140334713A1 (en) | Method and apparatus for constructing map for mobile robot | |
| CN112700479B (en) | Registration method based on CNN point cloud target detection | |
| CN113589306B (en) | Positioning method, positioning device, electronic equipment and storage medium | |
| CN114383622B (en) | Robot positioning method, robot, and computer-readable storage medium | |
| CN115240094A (en) | Garbage detection method and device | |
| CN110135278A (en) | A kind of obstacle detection method, device and electronic equipment | |
| CN113269811A (en) | Data fusion method and device and electronic equipment | |
| CN113475976A (en) | Method and device for determining passable area of robot, storage medium and robot | |
| CN112820141B (en) | Parking space detection method and system | |
| CN112965076A (en) | Multi-radar positioning system and method for robot | |
| CN112381873A (en) | Data labeling method and device | |
| CN115917624A (en) | Central device, map generation system, and map generation method | |
| CN115909253A (en) | Target detection and model training method, device, equipment and storage medium | |
| CN115507840A (en) | Grid map construction method, grid map construction device and electronic equipment | |
| CN107392209B (en) | Device and method for extracting line segments | |
| Tang et al. | An approach of dynamic object removing for indoor mapping based on UGV SLAM | |
| CN114755444B (en) | Target speed measuring method, target speed measuring device, electronic apparatus, and storage medium | |
| CN115293980B (en) | Small-size dynamic noise filtering method and device based on historical information |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |