CN112629518A - Laser map updating method, robot and clustered robot system - Google Patents

Laser map updating method, robot and clustered robot system Download PDF

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Publication number
CN112629518A
CN112629518A CN201910953341.3A CN201910953341A CN112629518A CN 112629518 A CN112629518 A CN 112629518A CN 201910953341 A CN201910953341 A CN 201910953341A CN 112629518 A CN112629518 A CN 112629518A
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point cloud
map
laser
updated
laser point
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刘俊斌
虞坤霖
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Syrius Technology Shenzhen Co Ltd
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Syrius Technology Shenzhen Co Ltd
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Priority to PCT/CN2020/114058 priority patent/WO2021068701A1/en
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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

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Abstract

The application discloses a laser map updating method, a robot and a clustered robot system. The method comprises the following steps: determining point clouds to be updated according to the splicing result of the laser point clouds and the map laser point clouds, updating the information of the point clouds to be updated to a laser map, if the difference between the updated laser map and the original map exceeds a preset first threshold value, giving up updating, and if not, keeping updating, better ensuring that the updated map and the map before updating do not have rotation translation and zooming, and laying a foundation for the normal operation of upper-layer business logic.

Description

Laser map updating method, robot and clustered robot system
Technical Field
The application relates to the technical field of mobile robot map updating, in particular to a laser map updating method, a robot and a clustered robot system.
Background
Existing laser navigation schemes all involve the use of maps and all rely on map invariance. In practical application, the method has certain robustness to environmental changes, especially the structural environment on which the laser sensor depends. However, in practical applications, the robustness is easily broken, and the solution to deal with is to update the map, so that the robustness of the algorithm is maintained, and the navigation accuracy is ensured.
However, the existing map updating schemes cannot ensure that the updated map and the map before updating do not have rotational translation and zooming, which is the basis for ensuring the normal operation of the upper layer service logic.
Content of application
Therefore, it is necessary to provide a laser map updating method, a robot and a clustered robot system to solve the technical problem that the updated map and the map before updating do not have rotational translation and zooming.
In order to achieve the above object, the present application provides a laser map updating method, including:
splicing the laser point cloud and the map laser point cloud, and determining the point cloud to be updated according to a splicing result; the map laser point cloud is a combination of a plurality of point clouds having a specific relation with the laser point cloud;
updating the information of the point cloud to be updated to a laser map according to the laser point cloud and the map laser point cloud to obtain an updated laser map;
and comparing the updated laser map with the original map, if the difference between the two maps exceeds a preset first threshold value, giving up the updated laser map, and otherwise, keeping the updated laser map.
In some embodiments, the splicing the laser point cloud and the map laser point cloud, and determining the point cloud to be updated according to the splicing result specifically includes:
and when the current laser point cloud is successfully spliced with the map laser point cloud, splicing the current laser point cloud with each point cloud in the map laser point cloud, and determining the current laser point cloud which is successfully spliced as the point cloud to be updated.
In some embodiments, the splicing the laser point cloud and the map laser point cloud, and determining the point cloud to be updated according to the splicing result specifically includes:
when the current laser point cloud and the map laser point cloud are successfully spliced, splicing the current laser point cloud and each point cloud in the map laser point cloud, and determining the current laser point cloud which is successfully spliced as a first point cloud to be updated;
when the splicing of the current laser point cloud and the map laser point cloud is unsuccessful, defining the current laser point cloud with the failed splicing as a key point cloud, and determining the key point cloud as a second point cloud to be updated, wherein the point cloud to be updated is a set of the first point cloud to be updated and the second point cloud to be updated.
In some embodiments, the updating the information of the point cloud to be updated to the laser map according to the laser point cloud and the map laser point cloud to obtain the updated laser map specifically includes:
projecting the laser point cloud to the pose of each point cloud in the map laser point cloud to generate a current projection point cloud;
discretizing the angle of each point cloud in the current projection point cloud and the map laser point cloud:
and traversing the current projection point cloud, judging whether a dynamic object is present or not and judging whether an obstacle/map is changed or not, and if not, replacing the corresponding point cloud on the map to be updated with the laser point cloud.
In some embodiments, the traversing the current projection point cloud, determining whether a dynamic object is present and determining whether an obstacle/map is changed, and if not, replacing a corresponding point cloud on the map to be updated with a laser point cloud, specifically including:
judging whether a first distance from any map laser point in all map laser points in the map laser point cloud to a laser emitting device of the map laser point cloud is greater than a second distance from a current laser point corresponding to the map laser point in all current laser points in the current laser point cloud to the laser emitting device of the map laser point cloud; if so, continuously judging whether the distance difference between the first distance and the second distance is larger than a second threshold value, if so, judging that the current laser point cloud corresponds to the dynamic object, and not updating the map; if the distance difference is smaller than a second threshold value, judging that the current laser point cloud does not correspond to the dynamic object, and replacing the corresponding point cloud on the map to be updated with the current laser point cloud;
and when the first distance is smaller than the second distance, judging that the current laser point cloud corresponds to the obstacle/map change, and replacing the corresponding point cloud on the map to be updated with the current laser point cloud.
In some embodiments, the method further comprises:
receiving at least one of the updated laser maps;
and updating the at least one updated laser map into the native original map in a time sequence.
In order to achieve the above object, the present application further provides a robot, where the robot includes a laser for acquiring the laser point cloud, and the robot is configured to perform the above laser map updating method.
To achieve the above object, the present application also proposes a robot, a clustered robot system, comprising a plurality of the above robots.
In some embodiments, one robot of the system receives the updated laser map sent by other robots, and updates a plurality of point cloud maps into a local original map according to time sequence; and sends the updated map to other robots.
In some embodiments, the system further comprises a central server for receiving the updated laser maps sent by one or more robots, the central server updating one or more of the updated laser maps into the original map in a time-ordered manner and sending the updated map to a plurality of the robots.
According to the laser map updating method, the robot and the clustered robot system, the point cloud to be updated is determined according to the splicing result of the laser point cloud and the map laser point cloud, then the information of the point cloud to be updated is updated to the laser map, if the difference between the updated laser map and the original map exceeds a preset first threshold value, the updating is abandoned, otherwise, the updating is kept, the situation that the updated map and the map before updating do not have rotational translation and zooming is better ensured, and a foundation is laid for the normal operation of upper-layer business logic.
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In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present application, and for those skilled in the art, other drawings can be obtained according to the structures shown in the drawings without creative efforts.
FIG. 1 is a flow chart of a laser map update method according to one embodiment of the present application;
FIG. 2 is a flow chart of a laser map update method according to another embodiment of the present application;
FIG. 3 is a flow chart of a laser map update method according to yet another embodiment of the present application;
FIG. 4 is a block diagram of a robot according to an embodiment of the present application;
FIG. 5 is a block diagram of the configuration of the clustered robot system of one embodiment of the present application;
fig. 6 is a block diagram of a clustered robot system according to another embodiment of the present application.
Detailed Description
The technical solutions in the embodiments of the present application will be described clearly and completely with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
It should be noted that all the directional indications (such as up, down, left, right, front, and rear … …) in the embodiment of the present application are only used to explain the relative position relationship between the components, the movement situation, and the like in a specific posture (as shown in the drawing), and if the specific posture is changed, the directional indication is changed accordingly.
In addition, the descriptions referred to as "first", "second", etc. in this application are for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicit ly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one such feature. In addition, technical solutions between various embodiments may be combined with each other, but must be realized by a person skilled in the art, and when the technical solutions are contradictory or cannot be realized, such a combination should not be considered to exist, and is not within the protection scope of the present application.
In this application, unless expressly stated or limited otherwise, the terms "connected," "secured," and the like are to be construed broadly, and for example, "secured" may be a fixed connection, a removable connection, or an integral part; can be mechanically or electrically connected; they may be directly connected or indirectly connected through intervening media, or they may be connected internally or in any other suitable relationship, unless expressly stated otherwise. The specific meaning of the above terms in the present application can be understood by those of ordinary skill in the art as appropriate.
According to the laser device on the robot body, in the moving process of the robot, the laser device emits laser, and the robot receives the reflected light of an object, so that the external things are sensed.
In the present application, a laser point cloud or simply a point cloud, a laser point cloud is a set of laser points photographed by a robot at a position, and a laser point cloud map is a set composed of a group of such laser point clouds.
As shown in fig. 1, a laser map updating method provided in an embodiment of the present application includes:
step 10, splicing the laser point cloud and the map laser point cloud, and determining the point cloud to be updated according to a splicing result; the map laser point cloud is a combination of a plurality of point clouds having a specific relation with the laser point cloud;
the laser point clouds can be collected by the robot in real time at present, and can also be a plurality of laser point clouds collected by the robot in preset time. The laser point cloud collected in real time is hereinafter referred to as the current laser point cloud. And splicing each laser point cloud and the map laser point cloud.
A plurality of point clouds having a specific relationship with the laser point cloud, where the specific relationship may be selected according to the distance between each map point cloud and the laser point cloud, or other means.
Step 20, updating the information of the point cloud to be updated to a laser map according to the laser point cloud and the map laser point cloud to obtain an updated laser map;
and step 30, comparing the updated laser map with the original map, if the difference between the two maps exceeds a preset first threshold value, abandoning the updated laser map, and otherwise, keeping the updated laser map.
Specifically, the comparison algorithm is any image matching algorithm. The difference between the two maps exceeds a preset first threshold, specifically: when the difference (in pixels) between the two maps is less than a first threshold. Typically the first threshold is in the range 5% -20% of the map size.
And comparing the updated laser map with the original map, and giving up updating if the updated laser map changes the map skeleton of the original map. Thereby avoiding that deviations in positioning do not make map updates wrong.
According to the laser map updating method provided by the embodiment of the application, the point cloud to be updated is determined according to the splicing result of the current laser point cloud and the map laser point cloud, then the information of the point cloud to be updated is updated to the laser map, if the difference between the updated laser map and the original map exceeds a preset first threshold value, the updating is abandoned, otherwise, the rotation, the translation and the zooming of the updated map and the map before updating are better ensured, and a foundation is laid for the normal operation of upper-layer business logic.
In some embodiments, determining the point cloud to be updated and the relative pose between the current point cloud and the point cloud to be updated uses a first strategy. The method comprises the following steps of splicing the current laser point cloud and the map laser point cloud, and determining the point cloud to be updated according to a splicing result, wherein the method specifically comprises the following steps:
and when the current laser point cloud is successfully spliced with the map laser point cloud, splicing the current laser point cloud with each point cloud in the map laser point cloud, and determining the current laser point cloud which is successfully spliced as the point cloud to be updated.
And splicing the current laser point cloud and the map laser point cloud, wherein when the current laser point cloud and the map laser point cloud are successfully spliced, the splicing result is successful. The definition of successful splicing means that the diagonal value of the covariance matrix of the splicing of the current laser point cloud and the corresponding point cloud in the map laser point cloud is less than a third threshold value. The third threshold value ranges are: 1e-6 to 1 e-2.
In the embodiment of the application, only the current laser point cloud which is successfully spliced is processed, the current laser point cloud is determined as the point cloud to be updated, and the map is updated in the subsequent steps.
The specific method for updating the map laser point cloud by using the current laser point cloud is as follows: at the current laser point cloud (S)current) Position and posture (P)current) Selecting a set of map point clouds around (
Figure BDA0002226438100000061
To be provided with
Figure BDA0002226438100000062
To express that each point cloud corresponds to a pose of
Figure BDA0002226438100000063
To be provided with
Figure BDA0002226438100000064
To represent), the selection method may be to separate the pose of the current laser point (i.e., from the pose of each map point cloud
Figure BDA0002226438100000065
) The distance between the two or other means. Then using the map point clouds
Figure BDA0002226438100000066
Combined generation of map laser point clouds (S)merged)，SmergedPosition and posture P ofmerged) Can be selected as
Figure BDA0002226438100000067
In a posture of
Figure BDA0002226438100000068
Other point cloud poses are also possible, but we choose here to use
Figure BDA0002226438100000069
Then using ScurrentAnd SmergedThe pose change between 2 point clouds is obtained by splicing, namely
Figure BDA00022264381000000610
When S iscurrentAnd SmergedWhen splicing is successful, S is reusedcurrentAnd each component SmergedPoint cloud of
Figure BDA00022264381000000611
Splicing again, if the splicing is successful, the splicing can be directly obtained
Figure BDA00022264381000000612
If the splicing fails, the
Figure BDA00022264381000000613
It is derived as follows:
Figure BDA00022264381000000614
because of the fact that
Figure BDA00022264381000000615
So that there are
Figure BDA00022264381000000616
In another embodiment, determining the point cloud to be updated and the relative pose between the current point cloud and the point cloud to be updated uses a second strategy. The method comprises the following steps of splicing the current laser point cloud and the map laser point cloud, and determining the point cloud to be updated according to a splicing result, wherein the method specifically comprises the following steps:
when the splicing of the current laser point cloud and the map laser point cloud is successful, determining the current laser point cloud as a first point cloud to be updated;
when the splicing of the current laser point cloud and the map laser point cloud fails, determining the failed current laser point cloud as a key point cloud, determining the key point cloud as a second point cloud to be updated, and determining a set of the first point cloud to be updated and the second point cloud to be updated as the point cloud to be updated.
The first strategy simply updates the map laser point clouds only for successful point clouds after the successful splicing of the current laser point clouds and the map laser point clouds, while the second strategy can update unsuccessful point clouds (key point clouds) through successful point clouds instead of successful point clouds. For clarity, we define a successfully stitched current point cloud as ScurrentPose PcurrentAt this ScurrentThe current point cloud, i.e. the key point cloud, which is failed to be spliced before until the last splicing is successful is represented as
Figure BDA0002226438100000071
Is used universally to
Figure BDA0002226438100000072
To show that they correspond to a pose of
Figure BDA0002226438100000073
Is used universally to
Figure BDA0002226438100000074
To indicate. Usually we set a maximum number of failures, n < the fourth threshold, which is adjusted according to the actual situation, typically ranging from 0 to 20. Due to the fact that
Figure BDA0002226438100000075
If the splicing is not successful, the increment between the key point cloud and the map laser point cloud corresponding to the key point cloud is ScurrentThe map-based stitching is derived, and here corresponds to the fact that the difference between the angle and the distance and the map laser point cloud meets a fifth threshold. The fifth threshold consists of 2 parts, an angle between +/-90 degrees and a distance between 0 and 5 meters.
The derivation process of the key point cloud for updating the map laser point cloud is as follows: suppose ScurrentIf the map is successfully spliced, the method includes
Figure BDA0002226438100000076
(see in particular the first strategy). For each failed splice before this splice succeeds
Figure BDA0002226438100000077
The map laser point cloud corresponding to it is selected first (using the fifth threshold),
Figure BDA0002226438100000078
to be provided with
Figure BDA0002226438100000079
For general expressions and their poses
Figure BDA00022264381000000710
Deriving each sum for the point clouds
Figure BDA00022264381000000711
Corresponding relative position, i.e.
Figure BDA00022264381000000712
Figure BDA00022264381000000713
In the second strategy, a key point cloud
Figure BDA00022264381000000714
The unsuccessfully spliced point cloud is not abandoned, but is required to be used for map updating, because the situation that the current laser point cloud and the point cloud in the map laser point cloud are unsuccessfully spliced due to the fact that the environment is changed is quite likely, and the situation is quite normal and is a very important part needing updating in the embodiment of the application.
Further, as shown in fig. 2, the updating the information of the point cloud to be updated to the laser map according to the laser point cloud and the map laser point cloud to obtain an updated laser map specifically includes:
step 21, projecting the current laser point cloud to the pose of each point cloud in the map laser point cloud to generate a current projection point cloud;
step 22, discretizing the angle of each laser point in the current projection point cloud and the map laser point cloud;
and step 23, traversing the current projection point cloud, judging whether a dynamic object is present and whether the barrier/map is changed, and if not, replacing the corresponding point cloud on the map to be updated with the laser point cloud.
And when the object is not determined to be a dynamic object and the obstacle/map is not determined to be changed, replacing the corresponding point cloud on the map to be updated with the laser point cloud.
Further, traversing the current projection point cloud, determining whether a dynamic object is present and determining whether an obstacle/map is changed, and if neither dynamic object is present, replacing the corresponding point cloud on the map to be updated with a laser point cloud, specifically including:
judging whether a first distance from any map laser point in all map laser points in the map laser point cloud to a laser emitting device of the map laser point cloud is greater than a second distance from a current laser point corresponding to the map laser point in all current laser points in the current laser point cloud to the laser emitting device of the map laser point cloud; if so, continuously judging whether the distance difference between the first distance and the second distance is larger than a second threshold (the range of the second threshold is adjusted according to the actual condition and is generally 5cm-30cm), if so, judging that the current laser point cloud corresponds to the dynamic object, and not updating the map; if the distance difference is smaller than a second threshold value, judging that the current laser point cloud does not correspond to the dynamic object, and replacing the corresponding point cloud on the map to be updated with the current laser point cloud;
and when the first distance is smaller than the second distance, judging that the current laser point cloud corresponds to the obstacle/map change, and replacing the corresponding point cloud on the map to be updated with the current laser point cloud.
Specifically, in this embodiment, the adopted strategy is as follows: all laser points of the current laser point cloud are updated in the following manner. As described above, a laser point cloud is a collection of laser points that the robot takes at a location. First of all by
Figure BDA0002226438100000081
Will ScurrentIs projected to SmapIn the pose of (1), the current projection point cloud (S ') is generated'current) Then to S'currentAnd SmapDiscretized by the angle of each point and then updated as described in the figure.
Go through S'currentEach laser spot in (a). In fig. 3, an origin O is defined as a position of the laser, and a curve AB represents a map laser point under a map laser point cloud; the curve CD represents the current laser point under the current laser point cloud; in the left half of fig. 3, a first distance from any map laser point of all map laser points in the map laser point cloud to its emitting laser, for example, the OA line segment and the OB line segment in fig. 3 each represent a first distance rr, X map laser points are on the defined curve AB, and the distance from any map laser point to the origin O is a first distance. A second distance from the current laser point corresponding to the map laser point to the laser emitting device of the current laser point cloud, for example, the OD line segment in fig. 3 represents the second distance, in the right half, Y current laser points are provided on the right part of the curve CD after the point P, and the distance from any current laser point to the origin O is the second distance rc.
The point P is used as a boundary, and in the left half part, the first distance is larger than the second distance; in the right half, the first distances are each less than the second distance; the point P is considered as the first distance equal to the second distance, and the point does not need to be updated.
In the left half of the first distance being larger than the second distance, the new point (the current laser point) is likely to be a dynamic object, and the determination continues with the second threshold T, see fig. 3. And continuously judging whether the distance difference between the first distance and the second distance is larger than a second threshold value, if so, judging that the current laser point cloud corresponds to the dynamic object, and not updating the map. And if the distance difference is smaller than a second threshold value, judging that the current laser point cloud does not correspond to the dynamic object, and replacing the corresponding point cloud on the map to be updated with the current laser point cloud.
And when the first distances are smaller than the right half parts of the second distances, judging that the current laser point clouds correspond to the obstacles/map changes, and replacing the corresponding point clouds on the map to be updated with the current laser point clouds. The first distance is smaller than the second distance, which indicates that a moving obstacle exists before or the map is changed, and the second threshold T is not seen in the updating process, namely, the current laser point cloud is directly used for replacing the corresponding point cloud on the map to be updated.
Therefore, the laser map updating method of the embodiment of the application can be used for dealing with dynamic objects.
The above-described embodiments may be a stand-alone (single-robot) map update scheme, a laser map update method performed on one machine, involving no interaction with other robots (except for map sharing and distribution). Of course, the embodiment of the present application may also be applied to a multi-machine updating scheme, that is, a plurality of robots update a laser map together.
As shown in fig. 4, when the subject performing the laser map updating method is a plurality of robots, the method further includes:
step 40, receiving at least one updated laser map;
and 50, updating the at least one updated laser map into the original map of the local machine according to time sequence.
Steps 40 and 50 may be performed on a robot or may be performed on a central server.
At regular intervals (e.g., every half day, every two days, every three days, every half week, every four days, or once per week), each robot then sends its respective updated portion to the central server (or to a robot). The central server (or a certain robot) receives the updated laser map sent by one or more robots to form one or more sections of local update maps, and the local update map sent by each robot carries a number and is received by the central server (or a certain robot).
The center (or a robot) will do the following: for each received partial update, it is added to the original map with a temporal ordering, and then steps 20 and 30 described in the standalone policy are performed.
Finally, the fused map may be sent to each robot. The map may be updated cooperatively by multiple robots. Each robot can calculate the difference between the original map and the path traveled by the robot, and then all the robots send the difference to a central processing place (which can be a server or a specific robot), and the central server runs a fusion algorithm to fuse the collected information and then distributes the updated map to each robot to complete the map updating process.
As shown in fig. 5, an embodiment of the present application further provides a robot 100, where the robot 100 includes a laser 101 for acquiring the laser point cloud, and the robot 100 is configured to perform the above-mentioned laser map updating method.
The embodiment of the present application further provides a clustered robot system 200, which includes a plurality of robots 100 as described above.
Optionally, one of the robots of the system receives the updated laser maps sent by the other robots, and updates the plurality of updated laser maps to a local original map according to time sequence; and sends the updated map to other robots.
Further, the system further includes a central server 201, where the central server 201 is configured to receive the updated laser map sent by one or more robots, and the central server updates the one or more updated laser maps into the original map in a time-ordered manner, and sends the updated and fused map to the multiple robots.
It should be noted that the robot and the clustered robot system proposed in the embodiment of the present application are based on the same inventive concept as the laser map updating method proposed in the embodiment of the method of the present application, and the technical contents of the embodiment of the method and the embodiment of the robot and the embodiment of the clustered robot system are mutually applicable, and are not described in detail herein.
The above description is only a preferred embodiment of the present application, and not intended to limit the scope of the present application, and all modifications and equivalents of the subject matter of the present application, which is intended to be covered by the claims and their equivalents, or which are directly or indirectly applicable to other related arts are intended to be included within the scope of the present application.

Claims (10)

1. A laser map updating method, the method comprising:
splicing the laser point cloud and the map laser point cloud, and determining the point cloud to be updated according to a splicing result; the map laser point cloud is a combination of a plurality of point clouds having a specific relation with the laser point cloud;
updating the information of the point cloud to be updated to a laser map according to the laser point cloud and the map laser point cloud to obtain an updated laser map;
and comparing the updated laser map with the original map, if the difference between the two maps exceeds a preset first threshold value, giving up the updated laser map, and otherwise, keeping the updated laser map.
2. The method according to claim 1, wherein the splicing of the laser point cloud and the map laser point cloud and the determination of the point cloud to be updated according to the splicing result specifically comprise:
and when the current laser point cloud is successfully spliced with the map laser point cloud, splicing the current laser point cloud with each point cloud in the map laser point cloud, and determining the current laser point cloud which is successfully spliced as the point cloud to be updated.
3. The method according to claim 1, wherein the splicing of the laser point cloud and the map laser point cloud and the determination of the point cloud to be updated according to the splicing result specifically comprise:
when the current laser point cloud and the map laser point cloud are successfully spliced, splicing the current laser point cloud and each point cloud in the map laser point cloud, and determining the current laser point cloud which is successfully spliced as a first point cloud to be updated;
when the splicing of the current laser point cloud and the map laser point cloud is unsuccessful, defining the current laser point cloud with the failed splicing as a key point cloud, and determining the key point cloud as a second point cloud to be updated, wherein the point cloud to be updated is a set of the first point cloud to be updated and the second point cloud to be updated.
4. The method according to claim 1, wherein the updating the information of the point cloud to be updated to a laser map according to the laser point cloud and the map laser point cloud to obtain an updated laser map specifically comprises:
projecting the laser point cloud to the pose of each point cloud in the map laser point cloud to generate a current projection point cloud;
discretizing the angle of each point cloud in the current projection point cloud and the map laser point cloud;
and traversing the current projection point cloud, judging whether a dynamic object is present or not and judging whether an obstacle/map is changed or not, and if not, replacing the corresponding point cloud on the map to be updated with the laser point cloud.
5. The method according to claim 4, wherein traversing the current projection point cloud, determining whether a dynamic object is present and determining whether an obstacle/map is changed, and if neither, replacing a corresponding point cloud on the map to be updated with a laser point cloud, specifically comprises:
judging whether a first distance from any map laser point in all map laser points in the map laser point cloud to a laser emitting device of the map laser point cloud is greater than a second distance from a current laser point corresponding to the map laser point in all current laser points in the current laser point cloud to the laser emitting device of the map laser point cloud; if so, continuously judging whether the distance difference between the first distance and the second distance is larger than a second threshold value, if so, judging that the current laser point cloud corresponds to the dynamic object, and not updating the map; if the distance difference is smaller than a second threshold value, judging that the current laser point cloud does not correspond to the dynamic object, and replacing the corresponding point cloud on the map to be updated with the current laser point cloud;
and when the first distance is smaller than the second distance, judging that the current laser point cloud corresponds to the obstacle/map change, and replacing the corresponding point cloud on the map to be updated with the current laser point cloud.
6. The method according to any one of claims 1-5, further comprising:
receiving at least one of the updated laser maps;
and updating the at least one updated laser map into the native original map in a time sequence.
7. A robot, characterized in that the robot comprises a laser for acquiring the laser point cloud, and the robot is used for executing the laser map updating method of any one of claims 1-5.
8. A clustered robot system comprising a plurality of robots as claimed in claim 7.
9. The clustered robot system as claimed in claim 8, wherein one of the robots of the system receives the updated laser maps sent by the other robots, and updates the plurality of point cloud maps into a local original map in time sequence; and sends the updated map to other robots.
10. The clustered robot system of claim 8 further comprising a central server for receiving the updated laser maps transmitted by one or more robots, the central server updating one or more of the updated laser maps into the original map in a time-ordered manner and transmitting the updated map to a plurality of the robots.
CN201910953341.3A 2019-10-09 2019-10-09 Laser map updating method, robot and clustered robot system Pending CN112629518A (en)

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