CN117031491A - Map construction method and device, automatic navigation trolley and electronic equipment - Google Patents
Map construction method and device, automatic navigation trolley and electronic equipment Download PDFInfo
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- 238000013507 mapping Methods 0.000 claims description 9
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S17/00—Systems using the reflection or reradiation of electromagnetic waves other than radio waves, e.g. lidar systems
- G01S17/88—Lidar systems specially adapted for specific applications
- G01S17/89—Lidar systems specially adapted for specific applications for mapping or imaging
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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
- G01C21/30—Map- or contour-matching
- G01C21/32—Structuring or formatting of map data
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Abstract
The application relates to a map construction method, a device, an automatic navigation trolley and electronic equipment, and relates to the technical field of map construction; constructing a target point cloud map according to the multi-frame point cloud information; constructing a target grid map according to multi-frame point cloud information; according to the probability value of the corresponding grid of each point cloud point in the target grid map, eliminating the dynamic point in the target point cloud map; and outputting the target point cloud map after the dynamic points are removed. The map construction method, the map construction device, the automatic navigation trolley and the electronic equipment provided by the embodiment of the application can accurately identify the dynamic points in the target point cloud map, improve the identification precision of the dynamic points, reduce the probability of subsequent false deletion, rapidly reject the dynamic points in the target point cloud map and effectively improve the rejection efficiency.
Description
Technical Field
The application relates to the technical field of map construction, in particular to a map construction method, a map construction device, an automatic navigation trolley and electronic equipment.
Background
In the SLAM (instant positioning and map construction) navigation process, a point cloud map based on laser radar point cloud is firstly required to be established according to environmental characteristics, and because pedestrians or vehicles possibly move in a map construction scene in the map construction process, a plurality of dynamic miscellaneous points are contained in the generated map, and in order to improve the precision of the map generated by water, the dynamic miscellaneous points in the map are required to be removed. In the prior art, in the process of removing dynamic miscellaneous points of a map, some auxiliary software is generally used for manually editing the map containing the dynamic miscellaneous points, so that the dynamic miscellaneous points in the map are gradually removed, the mode of removing the dynamic miscellaneous points not only consumes time and reduces the working efficiency, but also has lower recognition precision, and the real dynamic miscellaneous points in the map cannot be accurately judged, so that the situation of easy false deletion is caused.
Disclosure of Invention
In order to solve the technical problems, the embodiment of the application provides a map construction method, a map construction device, an automatic navigation trolley and electronic equipment, which can accurately identify dynamic points in a target point cloud map, improve the identification precision of the dynamic points, reduce the probability of subsequent false deletion, quickly reject the dynamic points in the target point cloud map and effectively improve rejection efficiency.
In a first aspect, a map construction method is provided, including:
acquiring multi-frame point cloud information;
constructing a target point cloud map according to the multi-frame point cloud information;
constructing a target grid map according to the multi-frame point cloud information; wherein each grid of the target grid map corresponds to a probability value representing the existence of a point cloud in the grid;
removing dynamic points in the target point cloud map according to the probability value of the grid corresponding to each point cloud point in the target point cloud map in the target grid map; and
and outputting the target point cloud map after the dynamic points are removed.
According to a first aspect of the present application, the constructing a target point cloud map according to a plurality of frames of the point cloud information includes:
converting all the point cloud points in the multi-frame point cloud information into a target coordinate system, and superposing to obtain the target point cloud map; the target coordinate system represents a coordinate system taking a starting point of an automatic navigation trolley when a map is built as an origin.
According to a first aspect of the present application, the constructing a target grid map according to a plurality of frames of the point cloud information includes:
an initial grid map is constructed by taking a starting point of an automatic navigation trolley when a map is built as an origin;
converting the point cloud information of each frame according to a ray tracing model; and
and inserting the converted point cloud information into the initial grid map, calculating the probability value corresponding to each grid in the initial grid map, and forming the target grid map.
According to a first aspect of the present application, after the building of the target grid map according to the plurality of frames of the point cloud information, the map building method further includes:
and converting the coordinate system of the target grid map so that the direction of the coordinate axis of the target grid map is the same as the direction of the coordinate axis of the target point cloud map.
According to a first aspect of the present application, the removing the dynamic point in the target point cloud map according to the probability value of the grid corresponding to each point cloud point in the target point cloud map in the target grid map includes:
mapping each point cloud point in the target point cloud map into a corresponding grid according to the size of each grid in the target grid map; and
and eliminating dynamic points in the target point cloud map according to the probability value corresponding to the grid of each point cloud point map.
According to a first aspect of the present application, the removing the dynamic points in the target point cloud map according to the probability values corresponding to the grids mapped by each point cloud point includes:
and if the probability value corresponding to the grid mapped by the target point cloud point is smaller than or equal to a preset threshold value, eliminating the target point cloud point as the dynamic point.
According to a first aspect of the present application, before the acquiring multi-frame point cloud information, the map construction method further includes:
controlling the automatic navigation trolley to start moving from the drawing building starting point;
the acquiring multi-frame point cloud information comprises the following steps:
acquiring scanning information of a laser radar;
and acquiring pose information of the automatic navigation trolley relative to the map building starting point.
In a second aspect, there is also provided a map construction apparatus, including:
the first acquisition module is configured to acquire multi-frame point cloud information;
the first construction module is configured to construct a target point cloud map according to the multi-frame point cloud information;
the second construction module is configured to construct a target grid map according to the multi-frame point cloud information; wherein each grid of the target grid map corresponds to a probability value representing the existence of a point cloud in the grid;
the first rejecting module is configured to reject dynamic points in the target point cloud map according to the probability value of the grid corresponding to each point cloud point in the target point cloud map in the target grid map; and
and the first output module is configured to output the target point cloud map after the dynamic points are removed.
In a third aspect, there is also provided an automatic navigation cart comprising:
a vehicle body;
the map construction device is arranged on the vehicle body.
In a fourth aspect, an embodiment of the present application provides an electronic device, including: a processor; and a memory for storing the processor-executable instructions; the processor is configured to execute the map construction method described in the foregoing embodiment.
According to the map construction method, the map construction device, the automatic navigation trolley and the electronic equipment, the multi-frame point cloud information is acquired, the target point cloud map and the target grid map are constructed according to the multi-frame point cloud information, then whether the point cloud point is a dynamic point or not is judged through the probability value of the corresponding grid of each point cloud point in the target grid map, the point cloud point judged to be the dynamic point is eliminated from the target point cloud map, then the target point cloud map after the dynamic point is eliminated is output, compared with a scheme of manually editing the dynamic point eliminating scheme, the dynamic point can be quickly identified according to the probability value of the corresponding grid of the point cloud point in the target grid map, the identification accuracy is high, then the dynamic point in the target point cloud map is quickly eliminated, and the eliminating process does not need manual editing, so that the eliminating efficiency of the dynamic point can be effectively improved.
Drawings
The above and other objects, features and advantages of the present application will become more apparent by describing embodiments of the present application in more detail with reference to the attached drawings. The accompanying drawings are included to provide a further understanding of embodiments of the application and are incorporated in and constitute a part of this specification, illustrate the application and together with the embodiments of the application, and not constitute a limitation to the application. In the drawings, like reference numerals generally refer to like parts or steps.
Fig. 1 is a flowchart of a map construction method according to an exemplary embodiment of the present application.
Fig. 2 is a flowchart of a map construction method according to another exemplary embodiment of the present application.
Fig. 3 is a schematic flow chart of constructing a target grid map according to multi-frame point cloud information according to an exemplary embodiment of the present application.
Fig. 4 is a flowchart of a map construction method according to another exemplary embodiment of the present application.
Fig. 5 is a flowchart of removing dynamic points in a target point cloud map according to probability values of corresponding grids of each point cloud point in the target grid map according to an exemplary embodiment of the present application.
Fig. 6 is a flowchart of removing dynamic points in a target point cloud map according to probability values of corresponding grids of each point cloud point in the target grid map according to another exemplary embodiment of the present application.
Fig. 7 is a flowchart of a map construction method according to another exemplary embodiment of the present application.
Fig. 8 is a block diagram of a map construction apparatus according to an exemplary embodiment of the present application.
Fig. 9 is a block diagram of a map construction apparatus according to another exemplary embodiment of the present application.
Fig. 10 is a block diagram of an automatic navigation cart according to an exemplary embodiment of the present application.
Fig. 11 is a block diagram of an electronic device according to an exemplary embodiment of the present application.
Detailed Description
Hereinafter, exemplary embodiments according to the present application will be described in detail with reference to the accompanying drawings. It should be apparent that the described embodiments are only some embodiments of the present application and not all embodiments of the present application, and it should be understood that the present application is not limited by the example embodiments described herein.
Fig. 1 is a flowchart of a map construction method according to an exemplary embodiment of the present application. As shown in fig. 1, the map construction method provided by the embodiment of the application includes:
s210: and acquiring multi-frame point cloud information.
Specifically, in the process of building a map, an AGV (automatic navigation vehicle) moves in a predetermined scene, a laser radar on the automatic navigation vehicle scans surrounding scenes, and correspondingly, the point cloud information may include point cloud information obtained by laser radar scanning, pose information of the automatic navigation vehicle relative to a starting point of building the map, confidence level of pose information of the automatic navigation vehicle under global coordinates of the scenes, and the like.
S220: and constructing a target point cloud map according to the multi-frame point cloud information.
Specifically, the multi-frame point cloud information can be overlapped to obtain a point cloud map representing a scene, and the overlapped point cloud map can be converted into a coordinate system according to requirements to obtain a target point cloud map. The specific transformed coordinate system will be described in detail later.
S230: and constructing a target grid map according to the multi-frame point cloud information.
Specifically, the target grid map includes a plurality of grids distributed in sequence, and each grid corresponds to a probability value representing the existence of a point cloud in the grid, where the probability value is calculated in the process of constructing the target grid map.
S240: and eliminating dynamic points in the target point cloud map according to the probability value of the corresponding grid of each point cloud point in the target grid map.
S250: and outputting the target point cloud map after the dynamic points are removed.
Specifically, because the target point cloud map and the target grid map are both constructed by multi-frame point cloud information, in practical application, a relationship between the target point cloud map and the target grid map can be established, so that each point cloud point in the target point cloud map can be mapped in a grid area of the target grid map correspondingly, the probability of each point cloud point in the target point cloud map in a corresponding grid can be determined, then according to the probability of the corresponding grid, whether the point cloud point in the target point cloud map is a dynamic point can be determined, the point cloud point determined as the dynamic point is removed from the target point cloud map, and a specific determination principle and a removal process are described in detail later.
It should be understood that, by executing step S240 and step S250, the dynamic points in the target point cloud map can be accurately identified, and the dynamic points in the target point cloud map can be quickly removed without manual editing, so that the removal efficiency of the dynamic points can be effectively improved compared with the case of manual editing.
According to the map construction method provided by the embodiment of the application, the multi-frame point cloud information is acquired, the target point cloud map and the target grid map are constructed according to the multi-frame point cloud information, then, whether the point cloud point is a dynamic point or not is judged through the probability value of the corresponding grid in the target grid map of each point cloud point in the target point cloud map, the point cloud point judged as the dynamic point is eliminated from the target point cloud map, then, the target point cloud map after the dynamic point is eliminated is output, compared with a scheme of manually editing the dynamic point elimination, the dynamic point can be quickly identified by means of the probability value of the corresponding grid in the target grid map of the point cloud point, the identification accuracy is higher, then, the dynamic point in the target point cloud map is quickly eliminated, and the elimination efficiency of the dynamic point can be effectively improved without manual editing in the elimination process.
Fig. 2 is a flowchart of a map construction method according to another exemplary embodiment of the present application. As shown in fig. 2, step S220 may include:
s221: and converting all the point cloud points in the multi-frame point cloud information into a target coordinate system, and overlapping to obtain a target point cloud map.
Specifically, when the drawing construction operation is started, the automatic navigation trolley starts to move from the starting point when the drawing is constructed, and the target coordinate system can be understood as a coordinate system taking the starting point when the automatic navigation trolley is constructed as the origin; in the process of drawing construction, the automatic navigation trolley moves in a scene, point cloud information obtained by laser radar scanning is based on a coordinate system taking the laser radar as a coordinate origin, and in order to accurately correspond multi-frame point cloud information to a real-time scene, the multi-frame point cloud information is required to be converted into a target coordinate system, so that each point cloud point in a target point cloud map can be conveniently mapped in a grid corresponding to the target grid map.
Fig. 3 is a schematic flow chart of constructing a target grid map according to multi-frame point cloud information according to an exemplary embodiment of the present application. As shown in fig. 3, step S230 may include:
s231: and constructing an initial grid map by taking a starting point of the automatic navigation trolley when the map is built as an origin.
Specifically, the initial grid map is constructed by taking the starting point of the automatic navigation trolley when the map is constructed as the original point, so that the target grid map obtained later and the target point cloud map are positioned in the same target coordinate system, and the corresponding mapping relationship between the target grid map and the target point cloud map is convenient to build later.
S232: and converting the point cloud information of each frame according to the ray tracing model.
It should be noted that, the specific algorithm of the ray tracing model and the process of converting the point cloud information using the ray tracing model are described in the related art, and the present application is not repeated.
S233: and inserting the converted point cloud information into an initial grid map, and calculating a probability value corresponding to each grid in the initial grid map to form a target grid map.
Specifically, the initial grid map includes a plurality of empty grids, the converted point cloud information is correspondingly inserted into the empty grids, and then in the inserting process, a probability value corresponding to each grid in the initial grid map can be calculated according to the point cloud information corresponding to each grid, so that a target grid map is formed. It should be understood that, after step S233 is performed, each grid in the obtained target grid map corresponds to a probability value of the existence of a point cloud in the grid.
Fig. 4 is a flowchart of a map construction method according to another exemplary embodiment of the present application. As shown in fig. 4, after step S230, the map construction method further includes:
s260: and converting the coordinate system of the target grid map so that the direction of the coordinate axis of the target grid map is the same as the direction of the coordinate axis of the target point cloud map.
Specifically, after the target point cloud map and the target grid map are constructed, although the target point cloud map and the target grid map are both located in the target coordinate system and the origin positions are coincident by converting the coordinate system of the target point cloud map, there may be a case that the coordinate axis directions of the converted target point cloud map and the converted target grid map are not uniform, which may result in that the point cloud points in the target point cloud map cannot be accurately mapped into the grids of the corresponding target grid map in the process of executing step S240, so it is necessary to execute step S260 before executing step S240, so that the coordinate axis directions of the target grid map are identical to the coordinate axis directions of the target point cloud map, which may facilitate the subsequent quick establishment of the correspondence relationship between the target point cloud map and the target grid map, and facilitate the improvement of the working efficiency.
The coordinate axis direction of the target grid map is the same as the coordinate axis direction of the target point cloud map, and it is understood that the X-axis direction of the target grid map is the same as and coincides with the X-axis direction of the target point cloud map, the Y-axis direction of the target grid map is the same as and coincides with the Y-axis direction of the target point cloud map, and the Z-axis direction of the target grid map is the same as and coincides with the Z-axis direction of the target point cloud map.
Fig. 5 is a flowchart of removing dynamic points in a target point cloud map according to probability values of corresponding grids of each point cloud point in the target grid map according to an exemplary embodiment of the present application. As shown in fig. 5, step S240 may include:
s241: and mapping each point cloud point in the target point cloud map into a corresponding grid according to the size of each grid in the target grid map.
S242: and removing the dynamic points in the target point cloud map according to the probability value corresponding to the grid mapped by each point cloud point.
Specifically, after the size of each grid in the target grid map is obtained, the area occupied by each grid in the target grid map in the coordinate system can be obtained, the specific position of the area occupied by each grid in the target coordinate system is determined according to the coordinate value of the boundary point of the occupied area, then the grid area corresponding to each point cloud point in the target point cloud map can be determined according to the coordinate value of each point cloud point in the target point cloud map, then the dynamic point in the target point cloud map is removed according to the probability value corresponding to the corresponding grid of each point cloud point map, and the specific removal principle is described in detail later.
Fig. 6 is a flowchart of removing dynamic points in a target point cloud map according to probability values of corresponding grids of each point cloud point in the target grid map according to another exemplary embodiment of the present application. As shown in fig. 6, step S242 may include:
s2421: if the probability value corresponding to the grid mapped by the target point cloud point is smaller than or equal to a preset threshold value, the target point cloud point is taken as a dynamic point to be removed.
Specifically, if the probability of the grid corresponding to the target point cloud point mapping is smaller than or equal to the preset threshold, the possibility of movement of the target point cloud point may be considered, that is, the first frame time is located in the first grid, the second frame time may be located in the second grid, so that the probability value of the grid corresponding to the target point cloud point mapping is lower, and in this case, the target point cloud point may be rejected as a dynamic point.
It should be understood that the preset threshold may be set according to practical situations, and the preset threshold is not particularly limited in the present application.
Fig. 7 is a flowchart of a map construction method according to another exemplary embodiment of the present application. As shown in fig. 7, before step S210, the map construction method further includes:
s270: and controlling the automatic navigation trolley to start moving from the drawing starting point.
Step S210 includes:
s211: and acquiring scanning information of the laser radar.
S212: and acquiring pose information of the automatic navigation trolley relative to the drawing starting point.
Specifically, the multi-frame point cloud information may include scanning information of the laser radar and pose information of the automatic navigation trolley relative to the map building starting point, and it should be understood that performing step S211 and step S212 may facilitate subsequent conversion of the target point cloud map into the aforementioned target coordinate system, so as to effectively improve overall working efficiency.
The pose information of the automatic navigation cart relative to the map building starting point may include direction information, distance information, rotation angle information, and the like of the automatic navigation cart relative to the map building starting point.
Fig. 8 is a block diagram of a map construction apparatus according to an exemplary embodiment of the present application. As shown in fig. 8, a map construction apparatus 400 provided in an embodiment of the present application includes: a first obtaining module 410 configured to obtain multi-frame point cloud information; a first construction module 420 configured to construct a target point cloud map according to multi-frame point cloud information; a second construction module 430 configured to construct a target grid map from the multi-frame point cloud information; wherein, each grid of the target grid map corresponds to a probability value representing the existence of point clouds in the grid; the first rejecting module 440 is configured to reject dynamic points in the target point cloud map according to probability values of corresponding grids of each point cloud point in the target point cloud map in the target grid map; and a first output module 450 configured to output the target point cloud map after the dynamic point is removed.
According to the map construction device provided by the embodiment of the application, the multi-frame point cloud information is acquired, the target point cloud map and the target grid map are constructed according to the multi-frame point cloud information, then, whether the point cloud point is a dynamic point or not is judged through the probability value of the corresponding grid in the target grid map of each point cloud point in the target point cloud map, the point cloud point judged as the dynamic point is eliminated from the target point cloud map, then, the target point cloud map after eliminating the dynamic point is output, compared with a scheme of manually editing the dynamic point elimination, the dynamic point can be quickly identified by means of the probability value of the corresponding grid in the target grid map of the point cloud point, the identification accuracy is higher, then, the dynamic point in the target point cloud map is quickly eliminated, and the elimination efficiency of the dynamic point can be effectively improved without manual editing in the elimination process.
Fig. 9 is a block diagram of a map construction apparatus according to another exemplary embodiment of the present application. As shown in fig. 9, in an embodiment, the first construction module 420 may be further configured to convert all the point cloud points in the multi-frame point cloud information into the target coordinate system, and superimpose the point cloud points to obtain the target point cloud map; the target coordinate system represents a coordinate system taking a starting point of an automatic navigation trolley when a map is built as an origin.
As shown in fig. 9, in an embodiment, the second construction module 430 may include a third construction module 431 configured to construct an initial grid map with a starting point of the auto-navigation cart when constructing the map as an origin; a first conversion module 432 configured to convert each frame of point cloud information according to a ray tracing model; and an inserting module 433 configured to insert the converted point cloud information into the initial grid map, calculate a probability value corresponding to each grid in the initial grid map, and form a target grid map.
As shown in fig. 9, in an embodiment, the map construction apparatus 400 may include a second conversion module 460 configured to convert the coordinate system of the target grid map such that the direction of the coordinate axis of the target grid map is the same as the direction of the coordinate axis of the target point cloud map.
As shown in fig. 9, in an embodiment, the first culling module 440 may include a mapping module 441 configured to map each point cloud point in the target point cloud map in a corresponding grid according to a size of each grid in the target grid map; and a second culling module 442 configured to cull the dynamic points in the target point cloud map according to the probability value corresponding to the grid mapped by each point cloud point.
As shown in fig. 9, in an embodiment, the second culling module 442 may be further configured to cull the cloud point of the target point as a dynamic point if the probability value corresponding to the grid having the cloud point mapping of the target point is less than or equal to the preset threshold.
As shown in fig. 9, in one embodiment, the map construction device 400 may include a motion control module 470 configured to control the movement of the automated navigation cart from a map construction origin; the first acquisition module 410 may include: a second acquisition module 411 configured to acquire scanning information of the lidar; a third obtaining module 412 is configured to obtain pose information of the automatic navigation cart relative to the origin of the map.
Fig. 10 is a block diagram of an automatic navigation cart according to an exemplary embodiment of the present application. As shown in fig. 10, an automatic navigation cart 600 provided in an embodiment of the present application includes: a vehicle body 610; the map construction apparatus 400 described above is provided on the vehicle body 610.
The automatic navigation trolley provided by the embodiment of the application comprises the map building device and has all functions of the map building device, and the beneficial effects of the automatic navigation trolley can be referred to the beneficial effects of the map building device.
Fig. 11 is a block diagram of an electronic device according to an exemplary embodiment of the present application. As shown in fig. 11, an electronic device 800 provided by an embodiment of the present application may be either or both of a first device and a second device, or a stand-alone device independent thereof, which may communicate with the first device and the second device to receive the acquired input signals therefrom.
The electronic device 800 includes: a processor 810 and a memory 820; the memory 820 is used to store processor 810 executable instructions and the processor 810 is used to execute the executable instructions to implement the map construction method as described previously.
The processor 810 may be a Central Processing Unit (CPU) or other form of processing unit having data processing and/or instruction execution capabilities, and may control other components in the electronic device 800 to perform desired functions.
Memory 820 may include one or more computer program products that may include various forms of computer-readable storage media, such as volatile memory and/or non-volatile memory. The volatile memory may include, for example, random Access Memory (RAM) and/or cache memory (cache), and the like. The non-volatile memory may include, for example, read Only Memory (ROM), hard disk, flash memory, and the like. One or more computer program instructions may be stored on the computer readable storage medium that can be executed by the processor 810 to implement the control methods and/or other desired functions of the various embodiments of the present application described above. Various contents such as an input signal, a signal component, a noise component, and the like may also be stored in the computer-readable storage medium.
In one example, the electronic device 800 may further include: an input device 830 and an output device 840, which are interconnected by a bus system and/or other form of connection mechanism (not shown).
Where the controller is a stand-alone device, the input means 830 may be a communication network connector for receiving the acquired input signals from the first device and the second device.
In addition, the input device 830 may also include, for example, a keyboard, a mouse, and the like.
The output device 840 may output various information to the outside, including the determined distance information, direction information, and the like. The output device 840 may include, for example, a display, speakers, a printer, and a communication network and remote output devices connected thereto, etc.
Of course, only some of the components of the electronic device 800 that are relevant to the present application are shown in fig. 11 for simplicity, components such as buses, input/output interfaces, etc. are omitted. In addition, the electronic device 800 may include any other suitable components depending on the particular application.
The computer program product may write program code for performing operations of embodiments of the present application in any combination of one or more programming languages, including an object oriented programming language such as Java, C++ or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computing device, partly on the user's device, as a stand-alone software package, partly on the user's computing device, partly on a remote computing device, or entirely on the remote computing device or server.
The computer readable storage medium may employ any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. The readable storage medium may include, for example, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples (a non-exhaustive list) of the readable storage medium would include the following: an electrical connection having one or more wires, a portable disk, a hard disk, random Access Memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
The basic principles of the present application have been described above in connection with specific embodiments, however, it should be noted that the advantages, benefits, effects, etc. mentioned in the present application are merely examples and not intended to be limiting, and these advantages, benefits, effects, etc. are not to be considered as essential to the various embodiments of the present application. Furthermore, the specific details disclosed herein are for purposes of illustration and understanding only, and are not intended to be limiting, as the application is not necessarily limited to practice with the above described specific details.
The block diagrams of the devices, apparatuses, devices, systems referred to in the present application are only illustrative examples and are not intended to require or imply that the connections, arrangements, configurations must be made in the manner shown in the block diagrams. As will be appreciated by one of skill in the art, the devices, apparatuses, devices, systems may be connected, arranged, configured in any manner. Words such as "including," "comprising," "having," and the like are words of openness and mean "including but not limited to," and are used interchangeably therewith. The terms "or" and "as used herein refer to and are used interchangeably with the term" and/or "unless the context clearly indicates otherwise. The term "such as" as used herein refers to, and is used interchangeably with, the phrase "such as, but not limited to.
It is also noted that in the apparatus, devices and methods of the present application, the components or steps may be disassembled and/or assembled. Such decomposition and/or recombination should be considered as equivalent aspects of the present application.
The previous description of the disclosed aspects is provided to enable any person skilled in the art to make or use the present application. Various modifications to these aspects will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other aspects without departing from the scope of the application. Thus, the present application is not intended to be limited to the aspects shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
The foregoing description has been presented for purposes of illustration and description. Furthermore, this description is not intended to limit embodiments of the application to the form disclosed herein. Although a number of example aspects and embodiments have been discussed above, a person of ordinary skill in the art will recognize certain variations, modifications, alterations, additions, and subcombinations thereof.
Claims (10)
1. A map construction method, comprising:
acquiring multi-frame point cloud information;
constructing a target point cloud map according to the multi-frame point cloud information;
constructing a target grid map according to the multi-frame point cloud information; wherein each grid of the target grid map corresponds to a probability value representing the existence of a point cloud in the grid;
removing dynamic points in the target point cloud map according to the probability value of the grid corresponding to each point cloud point in the target point cloud map in the target grid map; and
and outputting the target point cloud map after the dynamic points are removed.
2. The map construction method according to claim 1, wherein constructing a target point cloud map from a plurality of frames of the point cloud information comprises:
converting all the point cloud points in the multi-frame point cloud information into a target coordinate system, and superposing to obtain the target point cloud map; the target coordinate system represents a coordinate system taking a starting point of an automatic navigation trolley when a map is built as an origin.
3. The map construction method according to claim 1, wherein constructing the target grid map from the plurality of frames of the point cloud information includes:
an initial grid map is constructed by taking a starting point of an automatic navigation trolley when a map is built as an origin;
converting the point cloud information of each frame according to a ray tracing model; and
and inserting the converted point cloud information into the initial grid map, calculating the probability value corresponding to each grid in the initial grid map, and forming the target grid map.
4. The map construction method according to claim 1, characterized in that after the construction of the target grid map from the plurality of frames of the point cloud information, the map construction method further comprises:
and converting the coordinate system of the target grid map so that the direction of the coordinate axis of the target grid map is the same as the direction of the coordinate axis of the target point cloud map.
5. The map construction method according to claim 1, wherein the removing the dynamic point in the target point cloud map according to the probability value of the grid corresponding to each point cloud point in the target point cloud map in the target grid map comprises:
mapping each point cloud point in the target point cloud map into a corresponding grid according to the size of each grid in the target grid map; and
and eliminating dynamic points in the target point cloud map according to the probability value corresponding to the grid of each point cloud point map.
6. The map construction method according to claim 5, wherein the removing the dynamic points in the target point cloud map according to the probability value corresponding to the grid mapped by each point cloud point comprises:
and if the probability value corresponding to the grid mapped by the target point cloud point is smaller than or equal to a preset threshold value, eliminating the target point cloud point as the dynamic point.
7. The map construction method according to claim 1, characterized in that before the acquisition of the multi-frame point cloud information, the map construction method further comprises:
controlling the automatic navigation trolley to start moving from the drawing building starting point;
the acquiring multi-frame point cloud information comprises the following steps:
acquiring scanning information of a laser radar;
and acquiring pose information of the automatic navigation trolley relative to the map building starting point.
8. A map construction apparatus, characterized by comprising:
the first acquisition module is configured to acquire multi-frame point cloud information;
the first construction module is configured to construct a target point cloud map according to the multi-frame point cloud information;
the second construction module is configured to construct a target grid map according to the multi-frame point cloud information; wherein each grid of the target grid map corresponds to a probability value representing the existence of a point cloud in the grid;
the first rejecting module is configured to reject dynamic points in the target point cloud map according to the probability value of the grid corresponding to each point cloud point in the target point cloud map in the target grid map; and
and the first output module is configured to output the target point cloud map after the dynamic points are removed.
9. An automatic navigation cart, comprising:
a vehicle body;
the map construction apparatus according to claim 8, provided on the vehicle body.
10. An electronic device, comprising:
a processor;
a memory for storing the processor-executable instructions;
the processor is configured to execute the executable instructions to implement the mapping method of any one of claims 1 to 7.
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| Application Number | Priority Date | Filing Date | Title |
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| CN202310720805.2A CN117031491A (en) | 2023-06-16 | 2023-06-16 | Map construction method and device, automatic navigation trolley and electronic equipment |
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| CN202310720805.2A CN117031491A (en) | 2023-06-16 | 2023-06-16 | Map construction method and device, automatic navigation trolley and electronic equipment |
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117788593A (en) * | 2024-02-26 | 2024-03-29 | 苏州艾吉威机器人有限公司 | Method, device, medium and equipment for eliminating dynamic points in three-dimensional laser data |
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2023
- 2023-06-16 CN CN202310720805.2A patent/CN117031491A/en active Pending
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117788593A (en) * | 2024-02-26 | 2024-03-29 | 苏州艾吉威机器人有限公司 | Method, device, medium and equipment for eliminating dynamic points in three-dimensional laser data |
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