CN114216451B - Robot map updating method and device - Google Patents
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
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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
- G01C21/26—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
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Abstract
The invention relates to the technical field of robots, in particular to a method for updating a robot map, which comprises the following steps: acquiring a current laser frame of a current position of a robot and a K-dimensional tree, wherein each node of the K-dimensional tree carries a corresponding reference laser frame; after updating the confidence coefficient of the reference laser frame of each current node corresponding to the current position according to the current laser frame, aiming at each current node in the current node set, if the updated confidence coefficient of the reference laser frame of the current node is smaller than a confidence coefficient threshold value, the current laser frame is used as a target candidate laser frame to be added into the current node; after adding the target candidate laser frame, in the process of updating and adjusting the confidence coefficient of the reference laser frame and the confidence coefficient of the target candidate laser frame, if the adjusted confidence coefficient of the target candidate laser frame and the adjusted confidence coefficient of the reference laser frame meet the map updating condition, updating the historical map by using the target candidate laser frame. The method improves the updating efficiency of the robot map.
Description
Technical Field
The present invention relates to the field of robots, and in particular, to a method and an apparatus for updating a robot map.
Background
For robots for indoor services, due to decoration of a working scene or adjustment of a layout of a table and a chair, etc., a robot map of the robot needs to be updated in real time. If the map of the robot is not updated timely, the positioning quality of the robot is deteriorated along with the environmental change, and even the positioning of the robot is lost, so that the quality of robot work is reduced. The update method of the robot map generally comprises the steps of storing relevant sensor data during map construction when the indoor environment is deployed, and gradually updating the map by adopting an incremental map optimization mode, so that the problem of low update efficiency of the robot map is caused.
Disclosure of Invention
According to the method and the device for updating the robot map, the technical problem that in the prior art, the updating efficiency of the robot map is low is solved, the updating efficiency of the robot map is improved, the real-time updating of the robot map is guaranteed, and the service quality of the robot is optimized.
In a first aspect, an embodiment of the present invention provides a method for updating a robot map, including:
acquiring a current laser frame of a current position of a robot and a K-dimensional tree, wherein the K-dimensional tree is obtained according to a historical map of the robot, and each node of the K-dimensional tree carries a reference laser frame corresponding to each node;
After updating the confidence coefficient of the reference laser frame of each current node in the current node set corresponding to the current position according to the current laser frame, if the updated confidence coefficient of the reference laser frame of the current node is smaller than a confidence coefficient threshold value, the current laser frame is used as a target candidate laser frame to be added into the current node, wherein each current node in the current node set is a node of the K-dimensional tree;
and aiming at each current node in the current node set, after adding the target candidate laser frame, in the process of updating and adjusting the confidence coefficient of the reference laser frame and the confidence coefficient of the target candidate laser frame in real time, if the adjusted confidence coefficient of the target candidate laser frame and the adjusted confidence coefficient of the reference laser frame meet the map updating condition, updating the historical map by adopting the target candidate laser frame.
Preferably, the acquiring the K-dimensional tree of the robot includes:
after generating a voronoi diagram from the historical map, selecting a sampling point set from the voronoi diagram;
and constructing a K-dimensional tree according to the sampling point set, and taking each sampling point as a node of the K-dimensional tree, wherein each sampling point in the sampling point set stores a reference laser frame corresponding to each sampling point.
Preferably, the updating the confidence level of the reference laser frame of each current node in the current node set corresponding to the current position according to the current laser frame includes:
aiming at each current node in the current node set, acquiring the current area of the current laser frame and the reference area of the reference laser frame of the current node;
obtaining an area overlapping rate according to the current area and the reference area;
and if the area overlapping rate is not smaller than an overlapping rate threshold value, obtaining the updated confidence coefficient according to the current laser frame and the reference laser frame.
Preferably, the acquiring the current area of the current laser frame and the reference area of the reference laser frame of the node includes:
obtaining the current area according to a first bounding box of the current laser frame, wherein the first bounding box is a minimum frame bounding a laser point of the current laser frame;
and obtaining the reference area according to a second surrounding frame of the reference laser frame, wherein the second surrounding frame is a minimum frame surrounding a laser spot of the reference laser frame.
Preferably, the obtaining the updated confidence level according to the current laser frame and the reference laser frame includes:
After registering the current laser frame and the reference laser frame, a registration diagram is obtained;
and obtaining the updated confidence coefficient according to the matching point in the registration graph and the laser point of the reference laser frame, wherein the matching point is a point which is obtained after the laser point of the current laser frame is overlapped with the laser point of the reference laser frame and meets the matching condition.
Preferably, the adding the current laser frame as the target candidate laser frame to the node includes:
matching and correcting the current laser frames and a preset number of historical laser frames to obtain target candidate laser frames;
and adding the target candidate laser frame into the node, and resetting the confidence coefficient of the reference laser frame to a preset initial value.
Preferably, in the process of updating and adjusting the confidence coefficient of the reference laser frame and the confidence coefficient of the target candidate laser frame in real time, the method further comprises:
and if the adjusted confidence coefficient of the target candidate laser frame and the adjusted confidence coefficient of the reference laser frame do not meet the map updating condition, storing the adjusted confidence coefficient of the target candidate laser frame and the adjusted confidence coefficient of the reference laser frame.
Based on the same inventive concept, the present invention also provides an update apparatus of a robot map, including:
the acquisition module is used for acquiring a current laser frame of the current position of the robot and a K-dimensional tree, wherein the K-dimensional tree is obtained according to a historical map of the robot, and each node of the K-dimensional tree carries a reference laser frame corresponding to each node;
an adding module, configured to, after updating the confidence coefficient of the reference laser frame of each current node in the current node set corresponding to the current position according to the current laser frame, add the current laser frame as a target candidate laser frame to each current node in the current node set if the updated confidence coefficient of the reference laser frame of the current node is smaller than a confidence coefficient threshold, where each current node in the current node set is a node of the K-dimensional tree;
and the updating module is used for updating the historical map by adopting the target candidate laser frame if the adjusted confidence coefficient of the target candidate laser frame and the adjusted confidence coefficient of the reference laser frame meet the map updating condition in the process of updating and adjusting the confidence coefficient of the reference laser frame in real time after adding the target candidate laser frame for each current node in the current node set.
Based on the same inventive concept, in a third aspect, the present invention provides a computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the steps of the robot map updating method when executing the program.
Based on the same inventive concept, in a fourth aspect, the present invention provides a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements the steps of a robot map updating method.
One or more technical solutions in the embodiments of the present invention at least have the following technical effects or advantages:
in the embodiment of the invention, the method for updating the robot map is applied to robots, in particular to robots with indoor services. The method comprises the steps of firstly obtaining a current laser frame and a K-dimensional tree of a current position of a robot. Here, discretizing the history map of the robot to generate a K-dimensional tree, wherein each node in the K-dimensional tree carries a reference laser frame corresponding to each node, so as to provide a reliable and high-precision basis for subsequent updating of the history map, improve the updating efficiency of the robot map, and facilitate updating of the robot map. And then, updating the confidence coefficient of the reference laser frame of each current node in the current node set corresponding to the current position according to the current laser frame. Then, for each current node in the current node set, if the updated confidence coefficient of the reference laser frame of the current node is smaller than the confidence coefficient threshold value, the current laser frame is used as a target candidate laser frame to be added into the current node, wherein each current node in the current node set is a node of the K-dimensional tree. Finally, for each current node in the current node set, after adding the target candidate laser frame, in the process of updating and adjusting the confidence coefficient of the reference laser frame and the confidence coefficient of the target candidate laser frame in real time, if the adjusted confidence coefficient of the target candidate laser frame and the adjusted confidence coefficient of the reference laser frame meet the map updating condition, updating the historical map by using the target candidate laser frame. Therefore, the updating method provided by the embodiment of the invention has the characteristics of simplicity, high efficiency and high precision, improves the updating efficiency of the robot map, is convenient for updating the robot map, and ensures that the robot map is updated in real time.
Drawings
Various other advantages and benefits will become apparent to those of ordinary skill in the art upon reading the following detailed description of the preferred embodiments. The drawings are only for purposes of illustrating the preferred embodiments and are not to be construed as limiting the invention. Also throughout the drawings, like reference numerals are used to designate like parts. In the drawings:
fig. 1 is a schematic step flow diagram of a robot map updating method in an embodiment of the present invention;
fig. 2 shows a schematic structural diagram of a history map of a robot in an embodiment of the present invention;
FIG. 3 is a schematic diagram of a structure of a Veno diagram obtained from a historical map in an embodiment of the invention;
FIG. 4 is a schematic diagram of the structure of sampling points in a Veno diagram in an embodiment of the present invention;
FIG. 5 is a schematic diagram illustrating a structure of acquiring a current node according to an embodiment of the present invention;
FIG. 6 illustrates a schematic diagram of a selected current node in an embodiment of the invention;
FIG. 7 shows a schematic diagram of a reference laser frame of a selected current node in an embodiment of the invention;
FIG. 8 illustrates a schematic diagram of bounding boxes of a reference laser frame of a selected current node in an embodiment of the invention;
FIG. 9 shows a schematic of a registration map in an embodiment of the invention;
fig. 10 shows a block diagram of a robot map updating apparatus in an embodiment of the present invention;
fig. 11 shows a schematic structural diagram of a computer device in an embodiment of the present invention.
Detailed Description
Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.
Example 1
A first embodiment of the present invention provides a robot map updating method, as shown in FIG. 1, including:
s101, acquiring a current laser frame of a current position of a robot and a K-dimensional tree, wherein the K-dimensional tree is obtained according to a historical map of the robot, and each node of the K-dimensional tree carries a reference laser frame corresponding to each node;
s102, after updating the confidence coefficient of the reference laser frame of each current node in the current node set corresponding to the current position according to the current laser frame, aiming at each current node in the current node set, if the updated confidence coefficient of the reference laser frame of the current node is smaller than a confidence coefficient threshold value, adding the current laser frame as a target candidate laser frame into the current node, wherein each current node in the current node set is a node of a K-dimensional tree;
S103, after adding the target candidate laser frames, updating the historical map by using the target candidate laser frames if the adjusted confidence of the target candidate laser frames and the adjusted confidence of the reference laser frames meet the map updating condition in the process of updating and adjusting the confidence of the reference laser frames in real time for each current node in the current node set.
In this embodiment, the method for updating the robot map is applied to robots, particularly robots for indoor services. The method comprises the steps of firstly obtaining a current laser frame and a K-dimensional tree of a current position of a robot. Here, discretizing the history map of the robot to generate a K-dimensional tree, wherein each node in the K-dimensional tree carries a reference laser frame corresponding to each node, so as to provide a reliable and high-precision basis for subsequent updating of the history map, improve the updating efficiency of the robot map, and facilitate updating of the robot map. And then, updating the confidence coefficient of the reference laser frame of each current node in the current node set corresponding to the current position according to the current laser frame. Then, for each current node in the current node set, if the updated confidence coefficient of the reference laser frame of the current node is smaller than the confidence coefficient threshold value, the current laser frame is used as a target candidate laser frame to be added into the current node, wherein each current node in the current node set is a node of the K-dimensional tree. Finally, for each current node in the current node set, after adding the target candidate laser frame, in the process of updating and adjusting the confidence coefficient of the reference laser frame and the confidence coefficient of the target candidate laser frame in real time, if the adjusted confidence coefficient of the target candidate laser frame and the adjusted confidence coefficient of the reference laser frame meet the map updating condition, updating the historical map by using the target candidate laser frame. Therefore, the updating method of the embodiment has the characteristics of simplicity, high efficiency and high precision, improves the updating efficiency of the robot map, facilitates the updating of the robot map, and ensures the updating of the robot map in real time.
Next, the specific implementation steps of the robot map updating method provided in this embodiment will be described in detail with reference to fig. 1:
first, step S101 is executed to obtain a current laser frame of a current position of the robot and a K-dimensional tree, where the K-dimensional tree is obtained according to a history map of the robot, and each node of the K-dimensional tree carries a reference laser frame corresponding to each node.
Specifically, the current position of the robot is obtained by acquiring a current laser frame of the current position by the robot through a laser radar when the robot is at the current position. The specific way to obtain the K-Dimensional Tree (abbreviated as kd-Tree) of the robot is to select a sampling point set from the Veno diagram after generating the Veno diagram from the history map; and constructing a K-dimensional tree according to the sampling point set, and taking each sampling point as a node of the K-dimensional tree, wherein each sampling point in the sampling point set stores a reference laser frame corresponding to each sampling point.
Wherein, a history map of the robot is firstly generated into a Voronoi diagram (Voronoi diagram for short). The history map is an environment map pre-stored by the robot or an environment map updated by the robot last time, as shown in fig. 2. In fig. 2, the black lines are walls, and the white areas are areas where the robot can travel. The voronoi diagram is a skeleton diagram of a historical map. Taking the history map of fig. 2 as an example, the generated voronoi diagram is shown in fig. 3. In fig. 3, a white line is generated according to a drivable area of the robot.
After the voronoi diagram is obtained, a plurality of sampling points, namely a sampling point set, are selected from the voronoi diagram. When each sampling point is selected, a frame of reference laser frame is sampled for each sampling point. Taking fig. 3 as an example, sampling points are selected at set intervals along the white line in fig. 3, so as to obtain a sampling point set shown in fig. 4, and small black points in fig. 4 are sampling points. The set interval is set according to actual requirements.
After the sampling point set is obtained, a K-dimensional tree is generated according to the sampling point set, and each sampling point in the sampling point set is used as a node of the K-dimensional tree. The distance between any two nodes in the K-dimensional tree is not smaller than the set distance, and the set distance is set according to actual requirements. When each sampling point is selected, a frame of reference laser frame is sampled for each sampling point, namely, each sampling point in the sampling point set stores the reference laser frame corresponding to each sampling point, and then each node in the K-dimensional tree stores the reference laser frame corresponding to each node.
For example, sampling points a, B and C are selected from the voronoi diagram, so as to obtain a sampling point set, and when the sampling point a is selected, a frame of reference laser frame a is sampled for a frame; similarly, a reference laser frame B of B and a reference laser frame C of C are obtained. A K-dimensional tree was generated from A, B and C, and A, B and C were taken as nodes of the K-dimensional tree. On the K-dimensional tree, node A (i.e., sample point A) stores a, node B stores B, and node C stores C.
In this embodiment, the history map of the robot is discretized to generate a K-dimensional tree. The K-dimensional tree represents the running track of the robot, provides a reliable and high-precision basis for the subsequent updating of the historical map, improves the updating efficiency of the robot map, and facilitates the updating of the robot map, so that the robot is more accurate to position and higher in service quality.
Step S102 is executed, after the confidence coefficient of the reference laser frame of each current node in the current node set corresponding to the current position is updated according to the current laser frame, if the updated confidence coefficient of the reference laser frame of the current node is smaller than a confidence coefficient threshold value, the current laser frame is used as a target candidate laser frame to be added to the current node, wherein each current node in the current node set is a node of the K-dimensional tree;
specifically, after the current laser frame and the K-dimensional tree of the current position of the robot are acquired, the confidence coefficient of the reference laser frame of each current node in the current node set corresponding to the current position is updated according to the current laser frame. The current node is a node of a K-dimensional tree in a radius setting range by taking the current position of the robot as the center, and the setting radius is set according to actual requirements. For example, taking the K-dimensional tree of fig. 4 as an example, as shown in fig. 5, a black circle in fig. 5 is a current position of the robot, and a node of the K-dimensional tree in the radius r range is a current node in the current node set corresponding to the current position, with the current position as a center.
The specific process of updating the confidence level of the reference laser frame of each current node in the current node set is as follows:
aiming at each current node in a current node set, the current area of a current laser frame and the reference area of a reference laser frame of the current node are obtained in the first step; secondly, obtaining the area overlapping rate according to the current area and the reference area; and thirdly, judging the area overlapping rate. And if the area overlapping rate is not smaller than the overlapping rate threshold value, obtaining updated confidence coefficient of the reference laser frame according to the current laser frame and the reference laser frame. Wherein the overlap ratio threshold is set according to actual requirements, for example, the overlap ratio threshold is set to 60%.
In the first step, the specific process of obtaining the current area of the current laser frame and the reference area of the reference laser frame of the current node is that the current area is obtained according to a first bounding box of the current laser frame, wherein the first bounding box is the minimum frame of laser points bounding the current laser frame; and obtaining a reference area according to a second surrounding frame of the reference laser frame, wherein the second surrounding frame is the minimum frame surrounding the laser point of the reference laser frame.
In the K-dimensional tree of fig. 4, as shown in fig. 6, taking the node of the K-dimensional tree selected with the black circle in fig. 6 as an example, the reference laser frame of the node is shown in fig. 7 assuming that the node is the current node. In fig. 7, a black line is a wall body obtained by laser sampling at a position of the robot at the node, and the wall body is formed by a plurality of laser points. The small black dots in fig. 7 are this node. And obtaining the area of the reference laser frame of the node, namely the reference area, according to the bounding box of the reference laser frame of the node. The bounding box is shown in fig. 8, and the dashed line in fig. 8 is the bounding box, which is the smallest border that encloses all laser points in the reference laser frame. Similarly, the current area of the current laser frame is also obtained.
In the second step, the area overlapping rate is obtained according to the current area of the current laser frame and the reference area of the reference laser frame of the current node.
Specifically, the overlapping area X of the current area and the reference area is obtained from both. And obtaining the area overlapping rate delta according to the ratio of the overlapping area X to the reference area Y, namely delta=X/Y or delta= (X/Y) X Y, wherein Y is a weight coefficient, and the value range is 0-1.
In the third step, the area overlapping ratio is judged. And if the area overlapping rate is not smaller than the overlapping rate threshold value, obtaining updated confidence coefficient according to the current laser frame and the reference laser frame. The updated confidence coefficient process of the reference laser frame is that after registering the current laser frame and the reference laser frame, a registration chart is obtained; and obtaining updated confidence coefficient according to the matching point in the registration graph and the laser point of the reference laser frame, wherein the matching point is a point which is obtained after the laser point of the current laser frame is overlapped with the laser point of the reference laser frame and meets the matching condition.
Specifically, the current laser frame and the reference laser frame are registered by ICP (Iterative Closest Point) algorithm or other point cloud matching method, and a registration chart is obtained, as shown in P3 of fig. 9. After the registration map is obtained, the matching points are obtained. The matching point is a point which is obtained after the laser point of the current laser frame is overlapped with the laser point of the reference laser frame and meets the matching condition. The points meeting the matching condition can be points where the laser points of the current laser frame and the laser points of the reference laser frame coincide with each other, or points where the overlapping distance between the laser points of the current laser frame and the laser points of the reference laser frame is not greater than a set overlapping distance threshold, where the set overlapping distance threshold is set according to actual requirements.
Taking fig. 9 as an example, P1 in fig. 9 is the current laser frame, the small black point of P1 is the laser point of the current laser frame, P2 is the reference laser frame, and the small black point of P2 is the laser point of the reference laser frame. And after registering the P1 and the P2, a registration diagram P3 is obtained. The laser spot circled by the black circle in P3 is the matching spot.
And after the registration graph is obtained, obtaining the registration rate of the registration graph according to the matching points in the registration graph and the laser points of the reference laser frame. And obtaining the updated confidence coefficient of the reference laser frame according to the registration rate.
The method comprises the following steps: and obtaining a registration rate S according to the number Gp of the matching points and the number Gc of the laser points of the reference laser frame, wherein S=Gp/Gc or S= (Gp/Gc) x S, and S is a weight coefficient, and the value range is 0-1. And obtaining updated confidence according to the registration rate, as shown in a formula (1).
F2＝F1-(1-S) 2 +0.01 (1)
Wherein F2 is the confidence after updating, and F1 is the confidence before updating of the reference laser frame.
If the area overlapping rate is smaller than the overlapping rate threshold value, the confidence coefficient of the reference laser frame is not updated, and the confidence coefficient before the reference laser frame is maintained.
In the embodiment, the method for updating the confidence coefficient of the reference laser frame of the current node has the characteristics of simplicity, high efficiency and high precision, improves the updating efficiency of the robot map, facilitates the updating of the robot map, and ensures the updating of the robot map in real time.
After updating the confidence coefficient of the reference laser frame of each current node in the current node set corresponding to the current position according to the current laser frame, the updated confidence coefficient of the reference laser frame of each current node needs to be judged. If the updated confidence coefficient of the reference laser frame of the current node is smaller than a confidence coefficient threshold value, the current laser frame is used as a target candidate laser frame to be added into the current node, wherein each current node in the current node set is a node of the K-dimensional tree, and the confidence coefficient threshold value is set according to actual requirements. If the updated confidence coefficient of the reference laser frame of the current node is not smaller than the confidence coefficient threshold value, the target candidate laser frame does not need to be added to the current node.
The specific operation of adding the current laser frame as the target candidate laser frame into the node is that the current laser frame and the historical laser frames with the preset quantity are matched and corrected to obtain the target candidate laser frame; and adding the target candidate laser frames into the nodes, and resetting the confidence level of the reference laser frames to a preset initial value.
Specifically, the historical laser frame is an environmental laser frame acquired at the current position in the previous time when the robot acquires the current laser frame. The preset number is set according to actual requirements. And matching and correcting the current laser frames and the historical laser frames with a preset number by an ICP algorithm or other point cloud matching methods to obtain target candidate laser frames. Then, the target candidate laser frame is added to the node, and the confidence of the reference laser frame is reset to a preset initial value. The preset initial value is set according to actual requirements, for example, the preset initial value is set to 0.9.
Then, step S103 is executed, for each current node in the current node set, after adding the target candidate laser frame, in the process of updating and adjusting the confidence coefficient of the reference laser frame and the confidence coefficient of the target candidate laser frame in real time, if the adjusted confidence coefficient of the target candidate laser frame and the adjusted confidence coefficient of the reference laser frame meet the map updating condition, updating the history map by using the target candidate laser frame.
Specifically, for each current node in the current node set, after adding the target candidate laser frame, the process of adjusting the confidence level of the reference laser frame and the confidence level of the target candidate laser frame is updated in real time. The method of updating the confidence of the reference laser frame is the same as the method of updating the confidence of the target candidate laser frame.
For example, assume that after adding the target candidate laser frame, the confidence level of a certain current node is reset to a preset initial value of 0.9, and the initial value of the confidence level of the target candidate laser frame is 1. After the laser frame of the robot at the next moment is acquired, the confidence coefficient of the reference laser frame of the current node and the confidence coefficient of the target candidate laser frame are updated according to the laser frame at the next moment.
The confidence degree method for updating the reference laser frame of the current node is as follows: the area of the laser frame at the next moment and the reference area of the reference laser frame of the current node are acquired first. And obtaining the area overlapping rate of the reference laser frame according to the area of the laser frame at the next moment and the reference area. And if the area overlapping rate of the reference laser frame is not smaller than the overlapping rate threshold value, obtaining the confidence coefficient of the adjusted reference laser frame according to the laser frame at the next moment and the reference laser frame. If the area overlapping rate of the reference laser frame is smaller than the overlapping rate threshold value, updating and adjusting are not carried out on the confidence coefficient of the reference laser frame, and the confidence coefficient before the reference laser frame is maintained.
The method for updating the confidence coefficient of the target candidate laser frame of the current node comprises the following steps: the area of the laser frame at the next moment and the candidate area of the target candidate laser frame of the current node are acquired first. And obtaining the area overlapping rate of the target candidate laser frame according to the area of the laser frame at the next moment and the candidate area. And if the area overlapping rate of the target candidate laser frames is not smaller than the overlapping rate threshold value, obtaining the adjusted confidence coefficient of the target candidate laser frames according to the laser frames at the next moment and the target candidate laser frames. If the area overlapping rate of the target candidate laser frames is smaller than the overlapping rate threshold value, updating and adjusting are not carried out on the confidence coefficient of the target candidate laser frames, and the confidence coefficient before the target candidate laser frames is kept.
In the step S102, the specific process of updating the confidence coefficient of the reference laser frame of the current node and the confidence coefficient of the target candidate laser frame according to the current laser frame in the current node set corresponding to the current position is described, and is not repeated herein.
And in the process of updating and adjusting the confidence coefficient of the reference laser frame and the confidence coefficient of the target candidate laser frame in real time, if the adjusted confidence coefficient of the target candidate laser frame and the adjusted confidence coefficient of the reference laser frame meet the map updating condition, updating the historical map by using the target candidate laser frame. And if the adjusted confidence coefficient of the target candidate laser frame and the adjusted confidence coefficient of the reference laser frame do not meet the map updating condition, storing the adjusted confidence coefficient of the target candidate laser frame and the adjusted confidence coefficient of the reference laser frame.
Judging whether the adjusted confidence coefficient of the target candidate laser frame and the adjusted confidence coefficient of the reference laser frame meet the map updating condition or not, specifically: obtaining a confidence ratio according to the adjusted confidence of the target candidate laser frame and the adjusted confidence of the reference laser frame; and judging the opposite confidence ratio. If the confidence ratio is greater than the confidence ratio threshold, determining that the adjusted confidence of the target candidate laser frame and the adjusted confidence of the reference laser frame meet the map updating condition. The confidence ratio threshold is set according to actual requirements. And if the confidence ratio is not greater than the confidence ratio threshold, determining that the adjusted confidence of the target candidate laser frame and the adjusted confidence of the reference laser frame do not meet the map updating condition.
For example, the adjusted confidence coefficient of the target candidate laser frame is TB, the adjusted confidence coefficient of the reference laser frame is RB, and the confidence coefficient ratio BZ, that is, bz= (TB-RB)/RB is obtained according to the TB of the target candidate laser frame and the RB of the reference laser frame. When the confidence ratio is greater than the confidence ratio threshold, namely BZ > confidence ratio threshold M, determining that the adjusted confidence of the target candidate laser frame and the adjusted confidence of the reference laser frame meet the map updating condition. And when BZ is less than or equal to M, determining that the adjusted confidence coefficient of the target candidate laser frame and the adjusted confidence coefficient of the reference laser frame do not meet the map updating condition.
In this embodiment, it is determined whether to update the history map of the robot with the target candidate laser frame according to the target candidate laser frame and the reference laser frame of the current node. The method for updating the historical map according to the target candidate laser frame and the reference laser frame of the current node has the characteristics of simplicity, high efficiency and high precision, improves the updating efficiency of the robot map, facilitates the updating of the robot map, and ensures the updating of the robot map in real time.
Example two
Based on the same inventive concept, the second embodiment of the present invention further provides a robot map updating apparatus, as shown in fig. 10, including:
an obtaining module 201, configured to obtain a current laser frame of a current position of a robot and a K-dimensional tree, where the K-dimensional tree is obtained according to a historical map of the robot, and each node of the K-dimensional tree carries a reference laser frame corresponding to each node;
an adding module 202, configured to, after updating the confidence coefficient of the reference laser frame of each current node in the current node set corresponding to the current position according to the current laser frame, add the current laser frame as a target candidate laser frame to each current node in the current node set, if the updated confidence coefficient of the reference laser frame of the current node is smaller than a confidence coefficient threshold, where each current node in the current node set is a node of the K-dimensional tree;
And the updating module 203 is configured to update the historical map with the target candidate laser frame if the adjusted confidence coefficient of the target candidate laser frame and the adjusted confidence coefficient of the reference laser frame satisfy a map updating condition in a process of updating and adjusting the confidence coefficient of the reference laser frame in real time after adding the target candidate laser frame for each current node in the current node set.
As an alternative embodiment, the acquiring module 201, configured to acquire the K-dimensional tree of the robot, includes:
after generating a voronoi diagram from the historical map, selecting a sampling point set from the voronoi diagram;
and constructing a K-dimensional tree according to the sampling point set, and taking each sampling point as a node of the K-dimensional tree, wherein each sampling point in the sampling point set stores a reference laser frame corresponding to each sampling point.
As an optional embodiment, the adding module 202, configured to update, according to the current laser frame, a confidence level of a reference laser frame of each current node in the current node set corresponding to the current position, includes:
aiming at each current node in the current node set, acquiring the current area of the current laser frame and the reference area of the reference laser frame of the current node;
Obtaining an area overlapping rate according to the current area and the reference area;
and if the area overlapping rate is not smaller than an overlapping rate threshold value, obtaining the updated confidence coefficient according to the current laser frame and the reference laser frame.
As an optional embodiment, the acquiring the current area of the current laser frame and the reference area of the reference laser frame of the node includes:
obtaining the current area according to a first bounding box of the current laser frame, wherein the first bounding box is a minimum frame bounding a laser point of the current laser frame;
and obtaining the reference area according to a second surrounding frame of the reference laser frame, wherein the second surrounding frame is a minimum frame surrounding a laser spot of the reference laser frame.
As an optional embodiment, the obtaining the updated confidence level according to the current laser frame and the reference laser frame includes:
after registering the current laser frame and the reference laser frame, a registration diagram is obtained;
and obtaining the updated confidence coefficient according to the matching point in the registration graph and the laser point of the reference laser frame, wherein the matching point is a point which is obtained after the laser point of the current laser frame is overlapped with the laser point of the reference laser frame and meets the matching condition.
As an optional embodiment, the adding the current laser frame as the target candidate laser frame to the node includes:
matching and correcting the current laser frames and a preset number of historical laser frames to obtain target candidate laser frames;
and adding the target candidate laser frame into the node, and resetting the confidence coefficient of the reference laser frame to a preset initial value.
As an optional embodiment, the updating module 203 is configured to, in a process of updating and adjusting the confidence level of the reference laser frame and the confidence level of the target candidate laser frame in real time, further include:
and if the adjusted confidence coefficient of the target candidate laser frame and the adjusted confidence coefficient of the reference laser frame do not meet the map updating condition, storing the adjusted confidence coefficient of the target candidate laser frame and the adjusted confidence coefficient of the reference laser frame.
Since the update apparatus of the robot map described in this embodiment is an apparatus for implementing the update method of the robot map described in the first embodiment of the present application, based on the update method of the robot map described in the first embodiment of the present application, a person skilled in the art can understand the specific implementation of the update apparatus of the robot map of this embodiment and various modifications thereof, so how to implement the method of the first embodiment of the present application with respect to the update apparatus of the robot map will not be described in detail herein. The apparatus used by those skilled in the art to implement the method for updating a robot map in the first embodiment of the present application is within the scope of protection intended in the present application.
Example III
Based on the same inventive concept, the third embodiment of the present invention further provides a computer device, as shown in fig. 11, including a memory 304, a processor 302, and a computer program stored on the memory 304 and executable on the processor 302, where the processor 302 implements the steps of any one of the above-mentioned robot map updating methods when executing the program.
Where in FIG. 11, a bus architecture (represented by bus 300), bus 300 may comprise any number of interconnected buses and bridges, with bus 300 linking together various circuits, including one or more processors, represented by processor 302, and memory, represented by memory 304. Bus 300 may also link together various other circuits such as peripheral devices, voltage regulators, power management circuits, etc., as are well known in the art and, therefore, will not be described further herein. Bus interface 306 provides an interface between bus 300 and receiver 301 and transmitter 303. The receiver 301 and the transmitter 303 may be the same element, i.e. a transceiver, providing a means for communicating with various other apparatus over a transmission medium. The processor 302 is responsible for managing the bus 300 and general processing, while the memory 304 may be used to store data used by the processor 302 in performing operations.
Example IV
Based on the same inventive concept, the fourth embodiment of the present invention also provides a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements the steps of any one of the methods of the robot map updating method of the previous embodiment.
It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
While preferred embodiments of the present invention have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. It is therefore intended that the following claims be interpreted as including the preferred embodiments and all such alterations and modifications as fall within the scope of the invention.
It will be apparent to those skilled in the art that various modifications and variations can be made to the present invention without departing from the spirit or scope of the invention. Thus, it is intended that the present invention also include such modifications and alterations insofar as they come within the scope of the appended claims or the equivalents thereof.
Claims (9)
1. A method for updating a robot map, comprising:
acquiring a current laser frame of a current position of a robot and a K-dimensional tree, wherein the K-dimensional tree is obtained according to a historical map of the robot, and each node of the K-dimensional tree carries a reference laser frame corresponding to each node;
after updating the confidence coefficient of the reference laser frame of each current node in the current node set corresponding to the current position according to the current laser frame, if the updated confidence coefficient of the reference laser frame of the current node is smaller than a confidence coefficient threshold value, the current laser frame is used as a target candidate laser frame to be added into the current node, wherein each current node in the current node set is a node of the K-dimensional tree;
for each current node in the current node set, after adding the target candidate laser frame, in the process of updating and adjusting the confidence coefficient of the reference laser frame and the confidence coefficient of the target candidate laser frame in real time, if the adjusted confidence coefficient of the target candidate laser frame and the adjusted confidence coefficient of the reference laser frame meet the map updating condition, updating the historical map by using the target candidate laser frame;
Updating the confidence coefficient of the reference laser frame of each current node in the current node set corresponding to the current position according to the current laser frame, including:
aiming at each current node in the current node set, acquiring the current area of the current laser frame and the reference area of the reference laser frame of the current node;
obtaining an area overlapping rate according to the current area and the reference area;
and if the area overlapping rate is not smaller than an overlapping rate threshold value, obtaining the updated confidence coefficient according to the current laser frame and the reference laser frame.
2. The method of claim 1, wherein the acquiring a K-dimensional tree of a robot comprises:
after generating a voronoi diagram from the historical map, selecting a sampling point set from the voronoi diagram;
and constructing a K-dimensional tree according to the sampling point set, and taking each sampling point as a node of the K-dimensional tree, wherein each sampling point in the sampling point set stores a reference laser frame corresponding to each sampling point.
3. The method of claim 1, wherein the obtaining the current area of the current laser frame and the reference area of the reference laser frame of the node comprises:
Obtaining the current area according to a first bounding box of the current laser frame, wherein the first bounding box is a minimum frame bounding a laser point of the current laser frame;
and obtaining the reference area according to a second surrounding frame of the reference laser frame, wherein the second surrounding frame is a minimum frame surrounding a laser spot of the reference laser frame.
4. The method of claim 1, wherein the deriving the updated confidence level from the current laser frame and the reference laser frame comprises:
after registering the current laser frame and the reference laser frame, a registration diagram is obtained;
and obtaining the updated confidence coefficient according to the matching point in the registration graph and the laser point of the reference laser frame, wherein the matching point is a point which is obtained after the laser point of the current laser frame is overlapped with the laser point of the reference laser frame and meets the matching condition.
5. The method of claim 1, wherein the adding the current laser frame to the node as a target candidate laser frame comprises:
matching and correcting the current laser frames and a preset number of historical laser frames to obtain target candidate laser frames;
And adding the target candidate laser frame into the node, and resetting the confidence coefficient of the reference laser frame to a preset initial value.
6. The method of claim 5, wherein adjusting the confidence level of the reference laser frame and the confidence level of the target candidate laser frame in real-time updates further comprises:
and if the adjusted confidence coefficient of the target candidate laser frame and the adjusted confidence coefficient of the reference laser frame do not meet the map updating condition, storing the adjusted confidence coefficient of the target candidate laser frame and the adjusted confidence coefficient of the reference laser frame.
7. An update apparatus for a robot map, comprising:
the acquisition module is used for acquiring a current laser frame of the current position of the robot and a K-dimensional tree, wherein the K-dimensional tree is obtained according to a historical map of the robot, and each node of the K-dimensional tree carries a reference laser frame corresponding to each node;
an adding module, configured to, after updating the confidence coefficient of the reference laser frame of each current node in the current node set corresponding to the current position according to the current laser frame, add the current laser frame as a target candidate laser frame to each current node in the current node set if the updated confidence coefficient of the reference laser frame of the current node is smaller than a confidence coefficient threshold, where each current node in the current node set is a node of the K-dimensional tree;
The updating module is used for updating the historical map by adopting the target candidate laser frame if the adjusted confidence coefficient of the target candidate laser frame and the adjusted confidence coefficient of the reference laser frame meet a map updating condition in the process of updating and adjusting the confidence coefficient of the reference laser frame in real time after adding the target candidate laser frame for each current node in the current node set;
the adding module is specifically configured to:
aiming at each current node in the current node set, acquiring the current area of the current laser frame and the reference area of the reference laser frame of the current node;
obtaining an area overlapping rate according to the current area and the reference area;
and if the area overlapping rate is not smaller than an overlapping rate threshold value, obtaining the updated confidence coefficient according to the current laser frame and the reference laser frame.
8. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the method steps of any of claims 1-6 when the program is executed.
9. A computer readable storage medium having stored thereon a computer program, characterized in that the program when executed by a processor realizes the method steps of any of claims 1-6.
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