CN114993287B - Method and system for automatically updating map of cleaning robot - Google Patents
Method and system for automatically updating map of cleaning robot Download PDFInfo
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- 238000004140 cleaning Methods 0.000 title claims abstract description 35
- 238000000034 method Methods 0.000 title claims abstract description 18
- 238000013507 mapping Methods 0.000 claims abstract description 7
- 230000007423 decrease Effects 0.000 claims description 6
- 230000009466 transformation Effects 0.000 claims description 6
- 230000008859 change Effects 0.000 description 5
- 238000010276 construction Methods 0.000 description 4
- 230000003068 static effect Effects 0.000 description 3
- 238000013480 data collection Methods 0.000 description 2
- 230000007613 environmental effect 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
- G01C21/38—Electronic maps specially adapted for navigation; Updating thereof
- G01C21/3804—Creation or updating of map data
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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
- G01C21/38—Electronic maps specially adapted for navigation; Updating thereof
- G01C21/3804—Creation or updating of map data
- G01C21/3833—Creation or updating of map data characterised by the source of data
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
- Y02D10/00—Energy efficient computing, e.g. low power processors, power management or thermal management
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Abstract
The invention provides a method and a system for automatically updating a cleaning robot map, comprising the following steps: step S1: dividing the field environment into different areas according to preset requirements; step S2: marking points in the corresponding areas where the preset requirements are to be met; step S3: recording sensor data of the cleaning robot in a corresponding area, performing laser scanning when the robot enters a preset range of marking points, and matching the laser scanning points with known map points to obtain a matching score; when the matching score is larger than or equal to a preset value, the current environment is good; when the matching result is smaller than the preset value, the current environment is worse; step S4: based on the judgment of the current environment, judging whether the current map needs to be updated by using a Bayesian binary theorem; step S5: when the current map needs to be updated, mapping is performed based on the sensor data and the laser data.
Description
Technical Field
The invention relates to the technical field of cleaning robots, in particular to a method and a system for automatically updating a cleaning robot map.
Background
The autonomous navigation of the robot generally carries out slam map construction in advance, the constructed map is a static map, and only reflects the characteristics of the environment during map construction, however, the environment is dynamically changed in practice, such as the movement of a chair, a cabinet and a fixture, and the risk of positioning failure exists when the environment is not matched with the prior map after being changed. In particular, a cleaning robot for cleaning a room is required, and a relatively high positioning accuracy is required. To solve this problem, the static map needs to be updated periodically. The invention can enable the machine to judge whether the map needs to be updated in daily tasks and automatically update the map.
Patent document CN114271729a (application number: 202111404655.1) discloses a map construction method comprising: the cleaning robot device performs object feature recognition on the detected at least one target object to obtain object feature information of the target object; the cleaning robot device performs map construction based on the object feature information of the target object to obtain a map including the object feature information; and updating the map including the object feature information based on the newly detected object feature information of the target object and/or the detected change of the object feature information of the target object by the cleaning robot device.
Disclosure of Invention
Aiming at the defects in the prior art, the invention aims to provide a method and a system for automatically updating a cleaning robot map.
The method for automatically updating the map of the cleaning robot provided by the invention comprises the following steps:
step S1: dividing the field environment into different areas according to preset requirements;
Step S2: marking points in the corresponding areas where the preset requirements are to be met;
Step S3: recording sensor data of the cleaning robot in a corresponding area, performing laser scanning when the robot enters a preset range of marking points, and matching the laser scanning points with known map points to obtain a matching score; when the matching score is larger than or equal to a preset value, the current environment is good; when the matching result is smaller than the preset value, the current environment is worse;
step S4: based on the judgment of the current environment, judging whether the current map needs to be updated by using a Bayesian binary theorem;
step S5: when the current map needs to be updated, mapping is performed based on the sensor data and the laser data.
Preferably, the step S3 employs: and matching the laser scanning points with known map points by using an icp registration algorithm and/or lilkelihood to obtain a matched result.
Preferably, the step S4 employs:
when the current environment is poor, then the side variable l=l0+log (p (more)/1-p (more));
when the current environment is better, then the side variable l=l0+log (p (not more)/1-p (not more));
wherein L0 represents an initial value of the side variable; p (more) represents the update probability calculated by the current matching point; p (no more) represents the calculated non-update probability of the current matching point;
Obtaining the final value of the edge variable L through repeated matching iterative computation;
calculating S=1-1/(1+exp (L)), and as the L value continuously increases or decreases, the S value range is infinitely close to 0 or 1; when S is greater than the preset value, it is considered that the current map needs to be updated.
Preferably, the step S5 employs: after the cleaning robot task is finished, collecting coordinate transformation relation, odometer data and laser data of the robot, and generating a corresponding storage file; executing a map building program according to the generated corresponding storage file, and storing the map into a corresponding directory after completing map building; and loading the corresponding map for corresponding updating.
Preferably, when a plurality of areas need to be updated, the update task takes the form of a queue, and the updating is completed one by one.
The system for automatically updating the map of the cleaning robot provided by the invention comprises the following components:
module M1: dividing the field environment into different areas according to preset requirements;
Module M2: marking points in the corresponding areas where the preset requirements are to be met;
Module M3: recording sensor data of the cleaning robot in a corresponding area, performing laser scanning when the robot enters a preset range of marking points, and matching the laser scanning points with known map points to obtain a matching score; when the matching score is larger than or equal to a preset value, the current environment is good; when the matching result is smaller than the preset value, the current environment is worse;
module M4: based on the judgment of the current environment, judging whether the current map needs to be updated by using a Bayesian binary theorem;
Module M5: when the current map needs to be updated, mapping is performed based on the sensor data and the laser data.
Preferably, the module M3 employs: and matching the laser scanning points with known map points by using an icp registration algorithm and/or lilkelihood to obtain a matched result.
Preferably, the module M4 employs:
when the current environment is poor, then the side variable l=l0+log (p (more)/1-p (more));
when the current environment is better, then the side variable l=l0+log (p (not more)/1-p (not more));
wherein L0 represents an initial value of the side variable; p (more) represents the update probability calculated by the current matching point; p (no more) represents the calculated non-update probability of the current matching point;
Obtaining the final value of the edge variable L through repeated matching iterative computation;
calculating S=1-1/(1+exp (L)), and as the L value continuously increases or decreases, the S value range is infinitely close to 0 or 1; when S is greater than the preset value, it is considered that the current map needs to be updated.
Preferably, the module M5 employs: after the cleaning robot task is finished, collecting coordinate transformation relation, odometer data and laser data of the robot, and generating a corresponding storage file; executing a map building program according to the generated corresponding storage file, and storing the map into a corresponding directory after completing map building; and loading the corresponding map for corresponding updating.
Preferably, when a plurality of areas need to be updated, the update task takes the form of a queue, and the updating is completed one by one.
Compared with the prior art, the invention has the following beneficial effects:
1. the real-time automatic updating of the static map is ensured, and manual intervention is not needed, so that the positioning accuracy is ensured.
2. The current state and the current task of the robot are not affected, the background drawing can be automatically built as long as the data packet exists, and the robot resource can be reasonably utilized by only one drawing building task through queue form execution.
3. By automatically setting a matching judgment point at a place which is easy to change the environment when a task is executed, the matching degree of the environment is calculated in real time, and whether the environment has great change is automatically judged.
4. By means of recording the sensor data, when the environment is judged to have great change, map updating is immediately carried out, and the positioning and planning problems caused by the environment change are effectively solved.
5. The method for judging whether to update or not by setting the key points can be well applicable to various types of machines and working occasions, has good universality and reduces after-sales and operation maintenance costs;
Drawings
Other features, objects and advantages of the present invention will become more apparent upon reading of the detailed description of non-limiting embodiments, given with reference to the accompanying drawings in which:
fig. 1 is a flowchart of a method for automatically updating a map of a cleaning robot.
Detailed Description
The present invention will be described in detail with reference to specific examples. The following examples will assist those skilled in the art in further understanding the present invention, but are not intended to limit the invention in any way. It should be noted that variations and modifications could be made by those skilled in the art without departing from the inventive concept. These are all within the scope of the present invention.
Example 1
According to the method for automatically updating the map of the cleaning robot, as shown in fig. 1, the method comprises the following steps:
Step S1: dividing the field environment into different areas according to preset requirements; such as: dividing floors and rooms;
Step S2: marking points in the corresponding areas where the preset requirements are to be met; such as: marking points at the places where environmental changes easily occur according to the field environment, for example: the vicinity of the room doorway, the room interior, the hallway, the cabinet, etc.;
Step S3: recording sensor data of the cleaning robot in a corresponding area, performing laser scanning when the robot enters a preset range of marking points, and matching the laser scanning points with known map points to obtain a matching score; when the matching score is larger than or equal to a preset value, the current environment is good; when the matching result is smaller than the preset value, the current environment is worse; specifically, for example, a robot receives a task, starts recording from the departure of a charging pile, and records sensor data in sections or in floors or rooms if the current situation is cross-floor or cross-room; for example: if the tasks are the same layer, recording to the end of the tasks; if the floor is a cross floor, the floor recording is finished after the first floor enters the elevator, and the recording of the floor data needing to go to is restarted in the elevator. If two layers are removed, the two are recorded. Respectively judging whether the two layers are to be updated;
Step S4: based on the judgment of the current environment, judging whether the current map needs to be updated by using a Bayesian binary theorem; the Bayes binary theorem can effectively solve the jump situation of updating or not updating the boundary;
step S5: when the current map needs to be updated, mapping is performed based on the sensor data and the laser data.
Specifically, the step S3 employs: and (3) performing alignment matching on the laser scanning points and the known map points by using an icp registration algorithm, lilkelihood or other simple methods to obtain matching.
Specifically, the step S4 employs:
when the current environment is poor, then the side variable l=l0+log (p (more)/1-p (more));
when the current environment is better, then the side variable l=l0+log (p (not more)/1-p (not more));
wherein L0 represents an initial value of the side variable; p (more) represents the update probability calculated by the current matching point; p (no more) represents the calculated non-update probability of the current matching point;
Obtaining the final value of the edge variable L through repeated matching iterative computation;
calculating S=1-1/(1+exp (L)), and as the L value continuously increases or decreases, the S value range is infinitely close to 0 or 1; when S is greater than the preset value, it is considered that the current map needs to be updated.
For example: when l0=0.5; p (more) =0.6, p (not more) =1-p (more) =0.4;
When 10 points are to be matched in a certain area and the current environment is not considered to be good when the first matching is performed, l1=l0+log (p (more)/1-p (more))=0.5+log (0.6/0.4) =0.676; when the second match is considered that the current environment is better, l2=l1+log (p (not more)/1-p (not more))=0.676+log (0.4/0.6) =0.5; and so on, the value of the edge variable L at the 10 th match is calculated. Calculating the value of S according to the value of the side variable L; as the value of L becomes ever larger or smaller, the value range approaches-1 or 1 indefinitely, and when S is greater than a preset value (e.g., > 0.8), it is considered that a map update is required.
Specifically, the step S5 employs: and (3) returning the task to the charging pile after finishing, finishing data collection (recording the coordinate transformation relation, the odometer data and the laser data of the robot), and generating a corresponding storage file. And if the task is the cross-floor or room task, the task is correspondingly recorded and stored according to floor segmentation. And (3) judging that the map of the layer needs to be updated according to the S, executing a map building program, reading the data packet by the map building program to perform background map building, and storing the map into a corresponding directory after finishing map building. And judging whether to reload the map according to the task state of the robot, wherein a new map can be immediately validated in the non-task.
Specifically, when a plurality of areas need to be updated, the update task adopts a queue form, and the updating is completed one by one.
The system for automatically updating the map of the cleaning robot provided by the invention comprises the following components:
Module M1: dividing the field environment into different areas according to preset requirements; such as: dividing floors and rooms;
Module M2: marking points in the corresponding areas where the preset requirements are to be met; such as: marking points at the places where environmental changes easily occur according to the field environment, for example: the vicinity of the room doorway, the room interior, the hallway, the cabinet, etc.;
Module M3: recording sensor data of the cleaning robot in a corresponding area, performing laser scanning when the robot enters a preset range of marking points, and matching the laser scanning points with known map points to obtain a matching score; when the matching score is larger than or equal to a preset value, the current environment is good; when the matching result is smaller than the preset value, the current environment is worse; specifically, for example, a robot receives a task, starts recording from the departure of a charging pile, and records sensor data in sections or in floors or rooms if the current situation is cross-floor or cross-room; for example: if the tasks are the same layer, recording to the end of the tasks; if the floor is a cross floor, the floor recording is finished after the first floor enters the elevator, and the recording of the floor data needing to go to is restarted in the elevator. If two layers are removed, the two are recorded. Respectively judging whether the two layers are to be updated;
module M4: based on the judgment of the current environment, judging whether the current map needs to be updated by using a Bayesian binary theorem; the Bayes binary theorem can effectively solve the jump situation of updating or not updating the boundary;
Module M5: when the current map needs to be updated, mapping is performed based on the sensor data and the laser data.
Specifically, the module M3 employs: and (3) performing alignment matching on the laser scanning points and the known map points by using an icp registration algorithm, lilkelihood or other simple methods to obtain matching.
Specifically, the module M4 employs:
when the current environment is poor, then the side variable l=l0+log (p (more)/1-p (more));
when the current environment is better, then the side variable l=l0+log (p (not more)/1-p (not more));
wherein L0 represents an initial value of the side variable; p (more) represents the update probability calculated by the current matching point; p (no more) represents the calculated non-update probability of the current matching point;
Obtaining the final value of the edge variable L through repeated matching iterative computation;
calculating S=1-1/(1+exp (L)), and as the L value continuously increases or decreases, the S value range is infinitely close to 0 or 1; when S is greater than the preset value, it is considered that the current map needs to be updated.
For example: when l0=0.5; p (more) =0.6, p (not more) =1-p (more) =0.4;
When 10 points are to be matched in a certain area and the current environment is not considered to be good when the first matching is performed, l1=l0+log (p (more)/1-p (more))=0.5+log (0.6/0.4) =0.676; when the second match is considered that the current environment is better, l2=l1+log (p (not more)/1-p (not more))=0.676+log (0.4/0.6) =0.5; and so on, the value of the edge variable L at the 10 th match is calculated. Calculating the value of S according to the value of the side variable L; as the value of L becomes ever larger or smaller, the value range approaches-1 or 1 indefinitely, and when S is greater than a preset value (e.g., > 0.8), it is considered that a map update is required.
Specifically, the module M5 employs: and (3) returning the task to the charging pile after finishing, finishing data collection (recording the coordinate transformation relation, the odometer data and the laser data of the robot), and generating a corresponding storage file. And if the task is the cross-floor or room task, the task is correspondingly recorded and stored according to floor segmentation. And (3) judging that the map of the layer needs to be updated according to the S, executing a map building program, reading the data packet by the map building program to perform background map building, and storing the map into a corresponding directory after finishing map building. And judging whether to reload the map according to the task state of the robot, wherein a new map can be immediately validated in the non-task.
Specifically, when a plurality of areas need to be updated, the update task adopts a queue form, and the updating is completed one by one.
Those skilled in the art will appreciate that the systems, apparatus, and their respective modules provided herein may be implemented entirely by logic programming of method steps such that the systems, apparatus, and their respective modules are implemented as logic gates, switches, application specific integrated circuits, programmable logic controllers, embedded microcontrollers, etc., in addition to the systems, apparatus, and their respective modules being implemented as pure computer readable program code. Therefore, the system, the apparatus, and the respective modules thereof provided by the present invention may be regarded as one hardware component, and the modules included therein for implementing various programs may also be regarded as structures within the hardware component; modules for implementing various functions may also be regarded as being either software programs for implementing the methods or structures within hardware components.
The foregoing describes specific embodiments of the present application. It is to be understood that the application is not limited to the particular embodiments described above, and that various changes or modifications may be made by those skilled in the art within the scope of the appended claims without affecting the spirit of the application. The embodiments of the application and the features of the embodiments may be combined with each other arbitrarily without conflict.
Claims (8)
1. A method for automatically updating a map of a cleaning robot, comprising:
step S1: dividing the field environment into different areas according to preset requirements;
Step S2: marking points in the corresponding areas where the preset requirements are to be met;
Step S3: recording sensor data of the cleaning robot in a corresponding area, performing laser scanning when the robot enters a preset range of marking points, and matching the laser scanning points with known map points to obtain a matching score; when the matching score is larger than or equal to a preset value, the current environment is good; when the matching result is smaller than the preset value, the current environment is worse;
step S4: based on the judgment of the current environment, judging whether the current map needs to be updated by using a Bayesian binary theorem;
Step S5: when the current map needs to be updated, mapping is performed based on the sensor data and the laser data;
The step S4 employs:
when the current environment is poor, then the side variable l=l0+log (p (more)/1-p (more));
when the current environment is better, then the side variable l=l0+log (p (not more)/1-p (not more));
wherein L0 represents an initial value of the side variable; p (more) represents the update probability calculated by the current matching point; p (no more) represents the calculated non-update probability of the current matching point;
Obtaining the final value of the edge variable L through repeated matching iterative computation;
calculating S=1-1/(1+exp (L)), and as the L value continuously increases or decreases, the S value range is infinitely close to 0 or 1; when S is greater than the preset value, it is considered that the current map needs to be updated.
2. The method of automatic update of a cleaning robot map according to claim 1, wherein the step S3 employs: and matching the laser scanning points with known map points by using an icp registration algorithm and/or lilkelihood to obtain a matched result.
3. The method of automatic update of a cleaning robot map according to claim 1, wherein the step S5 employs: after the cleaning robot task is finished, collecting coordinate transformation relation, odometer data and laser data of the robot, and generating a corresponding storage file; executing a map building program according to the generated corresponding storage file, and storing the map into a corresponding directory after completing map building; and loading the corresponding map for corresponding updating.
4. The method of automatically updating a map of a cleaning robot according to claim 1, wherein when a plurality of areas all need to be updated, the update task takes a form of a queue, and the updating is completed one by one.
5. A system for automatically updating a map of a cleaning robot, comprising:
module M1: dividing the field environment into different areas according to preset requirements;
Module M2: marking points in the corresponding areas where the preset requirements are to be met;
Module M3: recording sensor data of the cleaning robot in a corresponding area, performing laser scanning when the robot enters a preset range of marking points, and matching the laser scanning points with known map points to obtain a matching score; when the matching score is larger than or equal to a preset value, the current environment is good; when the matching result is smaller than the preset value, the current environment is worse;
module M4: based on the judgment of the current environment, judging whether the current map needs to be updated by using a Bayesian binary theorem;
Module M5: when the current map needs to be updated, mapping is performed based on the sensor data and the laser data;
the module M4 employs:
when the current environment is poor, then the side variable l=l0+log (p (more)/1-p (more));
when the current environment is better, then the side variable l=l0+log (p (not more)/1-p (not more));
wherein L0 represents an initial value of the side variable; p (more) represents the update probability calculated by the current matching point; p (no more) represents the calculated non-update probability of the current matching point;
Obtaining the final value of the edge variable L through repeated matching iterative computation;
calculating S=1-1/(1+exp (L)), and as the L value continuously increases or decreases, the S value range is infinitely close to 0 or 1; when S is greater than the preset value, it is considered that the current map needs to be updated.
6. The system for automatically updating a map of a cleaning robot according to claim 5, wherein the module M3 employs: and matching the laser scanning points with known map points by using an icp registration algorithm and/or lilkelihood to obtain a matched result.
7. The system for automatically updating a map of a cleaning robot according to claim 5, wherein the module M5 employs: after the cleaning robot task is finished, collecting coordinate transformation relation, odometer data and laser data of the robot, and generating a corresponding storage file; executing a map building program according to the generated corresponding storage file, and storing the map into a corresponding directory after completing map building; and loading the corresponding map for corresponding updating.
8. The system for automatically updating a map of a cleaning robot according to claim 5, wherein when a plurality of areas are required to be updated, the update task is in a form of a queue, and the updating is completed one by one.
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| CN113932799A (en) * | 2021-10-29 | 2022-01-14 | 上海有个机器人有限公司 | Laser map update method, system, electronic device, medium and program product |
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| US11466992B2 (en) * | 2020-03-02 | 2022-10-11 | Beijing Baidu Netcom Science And Technology Co., Ltd. | Method, apparatus, device and medium for detecting environmental change |
| CN111486855B (en) * | 2020-04-28 | 2021-09-14 | 武汉科技大学 | Indoor two-dimensional semantic grid map construction method with object navigation points |
| CN113340296B (en) * | 2021-06-21 | 2024-04-09 | 上海仙工智能科技有限公司 | Method and device for automatically updating mobile robot map |
| CN114440855B (en) * | 2022-01-19 | 2022-08-23 | 深圳市神州云海智能科技有限公司 | Method and system for positioning and map updating in dynamic scene |
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| CN113503876A (en) * | 2021-07-09 | 2021-10-15 | 深圳华芯信息技术股份有限公司 | Multi-sensor fusion laser radar positioning method, system and terminal |
| CN113932799A (en) * | 2021-10-29 | 2022-01-14 | 上海有个机器人有限公司 | Laser map update method, system, electronic device, medium and program product |
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