CN115984504A - Map automatic updating method and system and storage medium - Google Patents
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Abstract
The invention provides a map automatic updating method and system, and a storage medium, wherein the method comprises the following steps: setting the area where the semi-static object is located as an area to be updated in a navigation map; after the mobile robot goes to a region to be updated of a map, starting a mapping algorithm, and collecting a positioning pose A of the mobile robot in a navigation map and a positioning pose B of the mobile robot in a local map; using the positioning poses B as vertexes, using pose transformation relations between two adjacent positioning poses B as two sides, constructing a graph model for graph optimization, and obtaining local graph construction positioning poses to construct a local map; performing timestamp alignment on the positioning pose A and the positioning pose B through linear interpolation compensation, and performing ICP alignment on the acquired track A and the track B to calculate a rotation translation matrix C so as to convert the local map into a navigation map coordinate system according to the matrix C; replacing the area to be updated of the map by the converted local map; thereby reducing the computing resources occupied by the map updating.
Description
Technical Field
The invention relates to a mobile robot positioning and navigation technology, in particular to a method, a system and a storage medium for automatically updating a map in a specific area where a semi-static object is located.
Background
At present, the intelligent degree of the field of factory and warehouse logistics is higher and higher, the mode of constructing an environment map by utilizing a laser SLAM technology and then conducting mobile robot navigation is more and more popular, and due to the fact that the implementation cost is low, deployment is simple and rapid, and more attention is paid.
However, in part of factory environments, due to the fact that objects in the environments often move and change, the actual environment and a laser profile map scanned in advance have large deviation, and therefore deviation occurs when the laser profile map is referred to in the actual operation process of the mobile robot, and the operation route and the position of the mobile robot can be inconsistent with the expectation.
For this purpose, various map updating techniques have been proposed in the prior art, such as a map updating scheme proposed in "laser map updating method, robot and clustered robot system" (patent publication No. CN 112629518A), which mainly adopts the steps of mapping the current laser point cloud onto the map according to the current positioning point, and then determining whether to update the map according to the total deviation between the current scanning point and the map point.
However, on one hand, when the laser radar with a higher sampling rate is used, the calculation amount is larger, and calculation is usually required all the time, so that the calculation resources are continuously occupied in a large map scene. On the other hand, in the step of confirming whether to update the map, the prior art judges by comparing the total deviation between the scanning point and the map point, which requires the calculation comparison of each laser point in the current scanning and the point on the map, and is very time-consuming and calculation resource-consuming.
Disclosure of Invention
Therefore, the present invention is directed to a method, a system, and a storage medium for automatically updating a map, so as to reduce the computing resources occupied by the map updating.
In order to achieve the above object, according to a first aspect of the present invention, there is provided an automatic map updating method, comprising the steps of:
step S100, setting an area where a semi-static object is located in a navigation map as an area to be updated of the map;
step S200, after the mobile robot goes to a region to be updated in a map, starting a mapping algorithm, and collecting a positioning pose A of the mobile robot in a navigation map and a positioning pose B of the mobile robot in a local map;
step S300, using the positioning poses B as vertexes, using pose transformation relations between two adjacent positioning poses B as double sides, constructing a graph model for graph optimization, and acquiring local graph construction positioning poses to construct a local map;
step S400, the positioning pose A and the positioning pose B are aligned through linear interpolation compensation time stamps, ICP (Iterative closed Point) alignment is carried out on the obtained track A and the track B, and a rotation translation matrix C is calculated to convert the local map into a navigation map coordinate system according to the matrix C;
and step S500, replacing the to-be-updated area of the map with the local map converted in the step S400.
In a possible preferred embodiment, step S500 further includes:
step S410, collecting the positioning pose A points on the track A
And the set of positioning pose B points on the track B->
Separately calculating the root mean square error
When judging
And allowing the step S500 to be performed when the preset precision value is satisfied.
In a possible preferred embodiment, in step S300, a map model is constructed to perform map optimization by using the positioning poses B of the key frames in the local map as vertices and pose transformation relationships between the positioning poses B of two adjacent key frames as two sides.
In a possible preferred embodiment, the key frame in step S500 is that when the mobile robot moves more than 0.5m or rotates more than 20 °, the current positioning pose is recorded as the positioning pose B of the key frame in the local map.
In order to achieve the above object, according to a second aspect of the present invention, there is provided an automatic map updating system, including:
the storage unit is used for storing a program comprising the steps of the automatic map updating method, so that the navigation unit, the data acquisition unit, the map building unit and the processing unit can be called and executed timely;
the navigation unit is used for setting the area where the semi-static object is located in the navigation map as an area to be updated in the map so as to enable the mobile robot to go ahead;
the map building unit is used for starting a map building algorithm in the area to be updated of the map;
the data acquisition unit is used for acquiring a positioning pose A of the mobile robot in the navigation map and a positioning pose B in the local map;
the processing unit is used for constructing a graph model to perform graph optimization by taking the key frame positioning poses B as vertexes and the pose transformation relation between two adjacent key frame positioning poses B as two sides, and acquiring local graph construction positioning poses;
the mapping unit is also used for constructing a local map according to the local mapping positioning pose;
the processing unit is also used for aligning the positioning pose A and the positioning pose B by linear interpolation compensation to time stamps, acquiring the track A and the track B, and performing ICP alignment to calculate a rotation translation matrix C so as to convert the local map into a navigation map coordinate system according to the matrix C;
and the map building unit is also used for updating the converted local map into the area to be updated of the navigation map.
In a possible preferred embodiment, the processing unit is further configured to: set the positioning pose A points on the track A
And a set of positioning pose B points on trajectory B +>
Separately calculating the root mean square error
When in use
Satisfy the preset essenceAnd when the value is the value, the map building unit is allowed to update the map.
In order to achieve the above object, according to a third aspect of the present invention, there is also provided a computer-readable storage medium having a computer program stored thereon, wherein the computer program is configured to, when executed by a processor, implement the steps of the automatic map updating method according to any one of the above.
By the automatic map updating method, the automatic map updating system and the storage medium, a map scene is ingeniously divided into three parts: the static map, the semi-static map and the dynamic map are used for processing only aiming at the semi-static map area by analyzing the using scene, and the specific area is divided for map updating, so that unnecessary calculated amount in the moving process of the mobile robot is effectively reduced, and meanwhile, the updating accuracy of the local map can be ensured to a certain extent under the reference of the original navigation map because the updated area is smaller than the whole map.
On the other hand, in a part of preferred schemes, because the prior art mainly judges whether to update the map by comparing the total deviation between the scanning points and the map points, each laser point in the current scanning and the points on the map need to be calculated and compared, which wastes time, but the invention can complete the comparison calculation by only calculating dozens of or even several points by recording a plurality of local map track points and navigation map track points, thereby greatly reducing the calculation requirement.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this application, are included to provide a further understanding of the invention, and are incorporated in and constitute a part of this specification. In the drawings:
FIG. 1 is a schematic diagram illustrating steps of an automatic map updating method according to the present invention;
FIG. 2 is a logic diagram illustrating an automatic map updating method according to the present invention;
FIG. 3 is a schematic diagram illustrating a linear interpolation in the automatic map updating method according to the present invention;
FIG. 4 is a diagram illustrating alignment of a track A and a track B in the automatic map updating method according to the present invention;
FIG. 5 is a schematic diagram illustrating a process of building an occupancy grid map in the automatic map updating method of the present invention;
FIG. 6 is a schematic structural diagram of an automatic map updating system according to the present invention.
Detailed Description
In order to make those skilled in the art better understand the technical solutions of the present invention, the following detailed description of the specific embodiments of the present invention will be given with reference to the accompanying examples to assist those skilled in the art to further understand the present invention. It should be apparent that the embodiments described herein are only a few embodiments of the present invention, and not all embodiments. It should be noted that the embodiments and features of the embodiments in the present application can be combined with each other without departing from the inventive concept and without conflicting therewith by those skilled in the art. All other embodiments, which can be derived by one of ordinary skill in the art from the embodiments given herein without making any creative effort, shall fall within the disclosure and scope of the present invention.
Furthermore, the terms "first," "second," "S100," "S200," and the like in the description and in the claims and the drawings of the present invention are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It should be understood that the data so used may be interchanged under appropriate circumstances such that embodiments of the invention described herein may be practiced in sequences other than those described. Also, the terms "including" and "having," as well as any variations thereof, are intended to cover non-exclusive inclusions. Unless expressly stated or limited otherwise, the terms "disposed," "mounted," "connected," and "connected" are to be construed broadly and may, for example, be fixedly connected, detachably connected, or integrally connected; can be mechanically or electrically connected; they may be connected directly or indirectly through intervening media, or they may be interconnected between two elements. The specific meaning of the above terms in this case can be understood by those skilled in the art in combination with the prior art as the case may be.
In the example of the present invention, for an industrial scene, the environment is generally divided into three types, a static environment, a semi-static environment, and a dynamic environment, wherein the static environment refers to a series of objects that do not move, such as wall pillars, the dynamic environment refers to objects that do not move, such as workers and vehicles, and the semi-static environment refers to objects whose positions change, but do not change all the time, such as cargoes.
In an industrial scene, dynamic objects are reduced after automation is realized, and a static environment is a main reference object for positioning, so that the patent mainly solves the problem of map updating of an area where semi-static objects are located. Since the positioning of mobile robots is mainly dependent on static objects, while semi-static objects and static objects interfere with the positioning, it is considered that in an automated factory, dynamic objects exist less, and more are semi-static objects. At present, a laser radar sensor is mainly adopted in the industry as a main positioning sensor, and for a processing scheme of a semi-static object, a reflective column is usually adopted as a positioning reference object, or a fixed reference object is additionally added, so that laser can scan more static objects, the former needs to mount a large number of reflective columns on a scene, and the latter needs to mount more fixed objects in the scene as the static reference object.
Therefore, the invention intends to adopt natural contour mapping and navigation, so that a reflective column does not need to be deployed in the scene, and for a semi-static scene, an additional static reference object does not need to be additionally installed, and the map is updated according to the preset scene, thereby effectively reducing CPU operation resources of the AGV, and calling a local mapping algorithm only when the AGV enters a region. Finally, the map can be updated in real time while fewer CPU (Central processing Unit) operation resources are occupied, the positioning accuracy in the semi-static area is guaranteed, and the map can be updated without additional operation as long as the automatic map updating area is set in the navigation map.
To this end, in a preferred embodiment, as shown in fig. 1 to 5, the present invention provides an automatic map updating method, comprising the steps of:
and step S100, setting the area where the semi-static object is located in the navigation map as an area to be updated of the map.
Specifically, in an example, the step S100 includes:
step S110, a navigation positioning map needs to be obtained first, so that a map construction needs to be performed on a scene, the map construction scheme adopted in this example is a karto slam algorithm, and after the map construction is completed, a 2D grid map of the scene is obtained.
And S120, editing the raster map obtained in the step S110 by using the Roboshop intelligent upper computer software, and setting the area where the semi-static object is located as an area to be updated in the map.
And step S200, after the mobile robot goes to the area to be updated of the map, starting a mapping algorithm, and collecting a positioning pose A of the mobile robot in the navigation map and a positioning pose B of the mobile robot in the local map.
Specifically, after the above work is completed, the mobile robot (hereinafter referred to as an AGV) can drive to the area to be updated in the map according to the navigation positioning map, and at this time, the mapping unit of the AGV starts a mapping algorithm, reads the laser radar data and the wheel-type mileage data, and starts to construct a local map. Meanwhile, in the process, the positioning pose A of the AGV in the navigation map and the positioning pose B in the local map are collected.
And step S300, constructing a graph model to perform graph optimization by taking the positioning poses B as vertexes and the pose transformation relation between two adjacent positioning poses B as two sides, and acquiring local graph construction positioning poses to construct a local map.
Specifically, after the AGV drives away from the area to be updated, the map updating system of the AGV closes the map building algorithm to perform back-end optimization on the local map, so as to construct the rasterized local map.
First, in this example, the back-end optimization approach is Graph optimization, which is one way to represent the optimization problem as a Graph (Graph) consisting of a number of vertices (Vertex) and edges (Edge) connecting these nodes. Where the quantity to be optimized (pose of the AGV) is represented by vertices and the constraints of the vertices (spatial constraints between two nodes) are represented by edges. The method aims to minimize the errors of prediction and observation by constructing a graph and optimizing the pose of each node.
In the local mapping of this example, not every mapping location pose is calculated, and in order to save computational resources, in this example, a key frame is used for calculation, and when the mobile robot translates for example more than 0.5m or rotates for more than 20 degrees, a key frame is selected.
And then, optimizing by taking the local mapping key frame positioning pose B of each timestamp of the local map as a vertex and taking the pose transformation relation between the positioning poses B of two adjacent key frames in the local map as two sides, and finally obtaining the optimized local mapping positioning pose to construct a rasterized local map.
Specifically, in the mapping process, the system records the positioning pose (vertex) of the key frame of the local mapping, the positioning pose transformation relation (bilateral) between adjacent key frames of the local mapping and the scanning point cloud information corresponding to the key frame. In the optimization process, the quantity to be optimized is the positioning pose (vertex) of the key frame in the local map, and the constraint is bilateral. After the optimization is completed, the optimized vertexes and the laser scanning points corresponding to each vertex can be obtained for performing local map construction.
In this example, the map used is a two-dimensional occupancy grid map. Firstly, an original grid map is constructed according to optimized vertexes, the map is divided into small grids according to the sizes of the grids, each grid in an original state is unknown, then the vertexes are placed into the map, the positions of scanning laser points of the vertexes are set to be occupied (black), middle grids spread by laser rays are set to be idle (white), all the vertexes and corresponding laser scanning points are sequentially placed into the grid map, the grid map is updated according to the black and the white, and the rasterized local map can be completed until all point positions are placed into the map. The process is shown in figure 5.
And S400, performing timestamp alignment on the positioning pose A and the positioning pose B through linear interpolation compensation, and performing ICP alignment on the acquired track A and the track B to calculate a rotation translation matrix C so as to convert the local map into a navigation map coordinate system according to the matrix C.
Specifically, after the local map is built, how to assemble the navigation map needs to be considered, and because the navigation map is different from the local map in coordinate system, in order to obtain the coordinate transformation relationship between the local map and the navigation map, the positioning pose B in the local map and the positioning pose a in the navigation map need to be aligned by time stamp.
In the present example, alignment uses a linear interpolation mode to compensate the pose, and two tracks under the same timestamp are finally obtained, namely a track a corresponding to a positioning pose a in a navigation map and a track B corresponding to a positioning pose B in a local map, and points on the tracks correspond to each other one by one, and the principle of linear interpolation is shown in fig. 3; assuming positioning pose of t-1 moment navigation map
Is->
Positioning pose of navigation map at time t->
Is->
And the positioning pose and position of the local map at s moment>
Is (` based on `)>
) Satisfy->
Then according to the linear interpolation, the positioning pose ≥ of the navigation map at the s moment can be obtained>
。
And aligning the two tracks by adopting an ICP (inductively coupled plasma) algorithm to obtain a translation and rotation matrix C of the two tracks, and transferring the local map to a navigation map coordinate system according to the matrix C.
The alignment of the time stamps is already carried out on the result obtained by linear interpolation, and at the moment, the positioning track point of the navigation map and the mapping positioning track point of the local mapping are in one-to-one correspondence, as shown in fig. 4, the right track point is the positioning point on the navigation map, the left track point is the positioning point on the local mapping, the points on the two tracks are in one-to-one correspondence, the rotation and translation transformation relation between the two tracks, namely the matrix C, is solved through an ICP algorithm, and the core idea is that the matrix C is just the rotation and translation transformation relation between the two tracks
Rotated and/or picked up>
And translating>
So that +>
And (3) overlapping, constructing an error function:
for minimising errors
：
The above equation is a standard least squares expression.
Finally, step S500, the local map converted in step S400 is used to replace the area to be updated of the map, and thus the local update of the navigation map can be completed.
On the other hand, in order to improve the overall map updating accuracy, it is necessary to judge whether the local map meets the deviation requirement before the map updating, and therefore in a preferred embodiment, the map automatic updating method further includes, before step S500:
step S410, adopting the root mean square error as the judgment standard of track alignment, and assuming that the tracks are aligned, collecting the positioning pose A points on the track A
And the set of positioning pose B points on the track B->
Separately calculating the root mean square error
Root mean square error
The smaller the RMSE at this time, the smaller the deviation between the two tracks, and it is preferable in this example that the two tracks are aligned better when the RMSE is less than 0.1m to allow step S500 to be performed.
On the other hand, corresponding to the above method example, as shown in fig. 6, the present invention further provides an automatic map updating system, which includes:
the storage unit is used for storing a program comprising the steps of the automatic map updating method, so that the navigation unit, the data acquisition unit, the map building unit and the processing unit can be called and executed timely;
the navigation unit is used for setting the area where the semi-static object is located in the navigation map as an area to be updated in the map so as to enable the mobile robot to go ahead;
the map building unit is used for starting a map building algorithm in a region to be updated of a map;
the data acquisition unit is used for acquiring a positioning pose A of the mobile robot in the navigation map and a positioning pose B in the local map;
the processing unit is used for constructing a graph model to perform graph optimization by taking the key frame positioning poses B as vertexes and the pose transformation relation between two adjacent key frame positioning poses B as two sides, and acquiring local graph construction positioning poses;
the mapping unit is also used for constructing a local map according to the local mapping positioning pose;
the processing unit is also used for aligning the positioning pose A and the positioning pose B by linear interpolation compensation to time stamps, acquiring the track A and the track B, and performing ICP alignment to calculate a rotation translation matrix C so as to convert the local map into a navigation map coordinate system according to the matrix C;
and the map building unit is also used for updating the converted local map into the area to be updated of the navigation map.
Further, in order to improve the overall map update accuracy, the processing unit is further configured to: set the positioning pose A points on the track A
And the set of positioning pose B points on the track B->
Separately calculating the root mean square error
When in use
And when the preset precision value is met, the map building unit is allowed to update the map.
In another aspect, corresponding to the above method, the present invention further provides a computer-readable storage medium, on which a computer program is stored, wherein the computer program, when executed by a processor, implements the steps of the automatic map updating method according to any one of the above methods.
In summary, the map scene is ingeniously divided into three parts by the map automatic updating method, the map automatic updating system and the map automatic updating storage medium provided by the invention: the static map, the semi-static map and the dynamic map are used for processing only aiming at the semi-static map area by analyzing the using scene, and the specific area is divided for map updating, so that unnecessary calculated amount in the moving process of the mobile robot is effectively reduced, and meanwhile, the updating accuracy of the local map can be ensured to a certain extent under the reference of the original navigation map because the updated area is smaller than the whole map.
On the other hand, in a part of preferred schemes, because the prior art mainly judges whether to update the map by comparing the total deviation between the scanning points and the map points, each laser point in the current scanning and the points on the map need to be calculated and compared, which wastes time, but the invention can complete the comparison calculation by only calculating dozens of or even several points by recording a plurality of local map track points and navigation map track points, thereby greatly reducing the calculation requirement.
The preferred embodiments of the invention disclosed above are intended to be illustrative only. The preferred embodiments are not intended to be exhaustive or to limit the invention to the precise embodiments disclosed. Obviously, many modifications and variations are possible in light of the above teaching. The embodiments were chosen and described in order to best explain the principles of the invention and the practical application, to thereby enable others skilled in the art to best utilize the invention. The invention is limited only by the claims and the full scope and equivalents thereof, and any modification, equivalent replacement, or improvement made within the spirit and principle of the invention should be included in the protection scope of the invention.
It will be appreciated by those skilled in the art that, in addition to implementing the system, apparatus and various modules thereof provided by the present invention in the form of pure computer readable program code, the same procedures may be implemented entirely by logically programming method steps such that the system, apparatus and various modules thereof provided by the present invention are implemented in the form of logic gates, switches, application specific integrated circuits, programmable logic controllers, embedded microcontrollers and the like. Therefore, the system, the device and the modules thereof provided by the present invention can be considered as a hardware component, and the modules included in the system, the device and the modules thereof for implementing various programs can also be considered as structures in the hardware component; modules for performing various functions may also be considered to be both software programs for performing the methods and structures within hardware components.
In addition, all or part of the steps of the method according to the above embodiments may be implemented by a program instructing related hardware, where the program is stored in a storage medium and includes several instructions to enable a single chip, a chip, or a processor (processor) to execute all or part of the steps of the method according to the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
In addition, any combination of various different implementation manners of the embodiments of the present invention is also possible, and the embodiments of the present invention should be considered as disclosed in the embodiments of the present invention as long as the combination does not depart from the spirit of the embodiments of the present invention.
Claims (7)
1. An automatic map updating method is characterized by comprising the following steps:
step S100, setting an area where a semi-static object is located in a navigation map as an area to be updated of the map;
step S200, after the mobile robot goes to a region to be updated in a map, starting a mapping algorithm, and collecting a positioning pose A of the mobile robot in a navigation map and a positioning pose B of the mobile robot in a local map;
step S300, using the positioning poses B as vertexes, using pose transformation relations between two adjacent positioning poses B as double sides, constructing a graph model for graph optimization, and acquiring local graph construction positioning poses to construct a local map;
step S400, performing timestamp alignment on the positioning pose A and the positioning pose B through linear interpolation compensation, and performing ICP alignment on the acquired track A and the track B to calculate a rotation translation matrix C so as to convert a local map into a navigation map coordinate system according to the matrix C;
and step S500, replacing the to-be-updated area of the map with the local map converted in the step S400.
2. The method for automatically updating the map according to claim 1, further comprising, before step S500:
step S410, a set of positioning pose A points on the track A
And the set of positioning pose B points on the track B->
Separately calculating the root mean square error
When judging
And allowing the step S500 to be performed when the preset precision value is satisfied.
3. The method of claim 1, wherein in step S300, a graph model is constructed to perform graph optimization by using the positioning poses B of the keyframes in the local map as vertices and pose transformation relationships between the positioning poses B of two adjacent keyframes as two sides.
4. The method according to claim 3, wherein the key frame in step S500 is that when the mobile robot moves more than 0.5m or rotates more than 20 °, the current positioning pose is recorded as the positioning pose B of the key frame in the local map.
5. An automatic map updating system, comprising:
a storage unit, for storing a program comprising the steps of the map automatic updating method according to any one of claims 1 to 4, so as to be used for the navigation unit, the data acquisition unit, the map building unit and the processing unit to timely call and execute;
the navigation unit is used for setting the area where the semi-static object is located in the navigation map as an area to be updated in the map so as to enable the mobile robot to go ahead;
the map building unit is used for starting a map building algorithm in the area to be updated of the map;
the data acquisition unit is used for acquiring a positioning pose A of the mobile robot in the navigation map and a positioning pose B in the local map;
the processing unit is used for constructing a graph model to perform graph optimization by taking the key frame positioning poses B as vertexes and the pose transformation relation between two adjacent key frame positioning poses B as two sides, and acquiring local graph construction positioning poses;
the mapping unit is also used for constructing a local map according to the local mapping positioning pose;
the processing unit is also used for aligning the positioning pose A and the positioning pose B by linear interpolation compensation to time stamps, acquiring the track A and the track B, and performing ICP alignment to calculate a rotation translation matrix C so as to convert the local map into a navigation map coordinate system according to the matrix C;
and the map building unit is also used for updating the converted local map into the area to be updated of the navigation map.
6. The automatic map updating system of claim 5, wherein the processing unit is further configured to: set positioning pose A points on the track A
And the set of positioning pose B points on the track B->
Separately calculating the root mean square error
When in use
And when the preset precision value is met, the map building unit is allowed to update the map.
7. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the automatic map updating method according to any one of claims 1 to 4.
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Denomination of invention: A Method, System, and Storage Medium for Automatic Map Update Effective date of registration: 20230828 Granted publication date: 20230704 Pledgee: Bank of Communications Ltd. Shanghai New District Branch Pledgor: Shanghai Xiangong Intelligent Technology Co.,Ltd. Registration number: Y2023310000491 |