CN114646936B - Point cloud map construction method, device and electronic device - Google Patents
Point cloud map construction method, device and electronic device Download PDFInfo
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- 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
- G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
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- G—PHYSICS
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- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
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Abstract
本申请公开了点云地图构建方法、装置及电子设备，属于点云地图构建技术领域，所述方法包括：在无人驾驶车按照预设路线移动过程中，采集环境点云信息生成第一局部点云地图；滤除第一局部点云地图中的预设静态目标，得到第二局部点云地图；滤除第二局部点云地图中的动态目标，得到第三局部点云地图；基于无人驾驶车按照预设路线移动过程中产生的各第三局部点云地图，构建目标点云地图。通过本申请公开的点云地图构建方案，能够滤除点云地图中的动态目标和参考价值较小的静态目标，得到高质量的点云地图，使得后续定位时得到更精准的位置信息。
The present application discloses a point cloud map construction method, device and electronic device, belonging to the field of point cloud map construction technology. The method includes: when the driverless vehicle moves along a preset route, collecting environmental point cloud information to generate a first local point cloud map; filtering out preset static targets in the first local point cloud map to obtain a second local point cloud map; filtering out dynamic targets in the second local point cloud map to obtain a third local point cloud map; and constructing a target point cloud map based on each third local point cloud map generated during the driverless vehicle's movement along the preset route. Through the point cloud map construction scheme disclosed in the present application, dynamic targets and static targets with less reference value in the point cloud map can be filtered out to obtain a high-quality point cloud map, so that more accurate location information can be obtained during subsequent positioning.
Description
Technical Field
The embodiment of the invention relates to the technical field of point cloud map construction, in particular to a point cloud map construction method, a point cloud map construction device and electronic equipment.
Background
In the field of unmanned vehicle driving, scene information is typically expressed using a point cloud map. The point cloud map generally comprises a plurality of point cloud points with semantic information and three-dimensional position information of each point cloud point under a world coordinate system, wherein the point cloud points can be used for indicating specific distribution conditions of different objects in space under a certain scene.
In the actual application process, the unmanned vehicle establishes a point cloud map according to the previously acquired environmental point cloud information, and the characteristics of the currently acquired point cloud information are matched with the point cloud map so as to achieve the purpose of positioning. However, this positioning method has a problem that the positioning accuracy is low in the subsequent positioning process because the surrounding environment is dynamic when the unmanned vehicle builds the point cloud map, for example, when a dynamic target exists.
Disclosure of Invention
The embodiment of the application aims to provide a point cloud map construction method, a device and electronic equipment, which can solve the problem that in the prior art, the positioning accuracy of a point cloud map established by an unmanned vehicle is low in the subsequent positioning process.
In order to solve the technical problems, the application is realized as follows:
In a first aspect, an embodiment of the present application provides a method for constructing a point cloud map, including:
Acquiring environmental point cloud information to generate a first local point cloud map in the moving process of the unmanned vehicle according to a preset route;
Filtering a preset static target in the first partial point cloud map to obtain a second partial point cloud map;
Filtering out a dynamic target in the second partial point cloud map to obtain a third partial point cloud map;
and constructing a target point cloud map based on each third local point cloud map generated in the process that the unmanned vehicle moves according to the preset route.
Optionally, the step of acquiring environmental point cloud information to generate the first local point cloud map in the moving process of the unmanned vehicle according to the preset route includes:
In the moving process of the unmanned vehicle according to a preset route, a laser radar installed on the unmanned vehicle determines the distance between the laser radar and each object in the surrounding environment based on the time interval of the transmitted and received pulse signals, and the distance is used as acquired environment point cloud information;
Generating a first local point cloud map based on the collected environmental point cloud information, wherein the first local point cloud map is used for representing environmental information around the unmanned vehicle at a certain moment.
Optionally, the step of filtering the preset static target in the first local point cloud map to obtain a second local point cloud map includes:
performing target segmentation processing on the first local point cloud map to obtain objects contained in the first local point cloud map, and performing color marking processing on the objects, wherein each object corresponds to a plurality of point cloud information, each point cloud information corresponds to a distance value, and color marking is performed on the objects contained in the first local point cloud map based on a preset corresponding relation between colors and distances when color marking is performed;
And filtering preset static targets in the objects after color marking to obtain the second local point cloud map.
Optionally, the step of filtering the dynamic target in the second local point cloud map to obtain a third local point cloud map includes:
determining a dynamic target based on point cloud information in a first local point cloud map acquired twice successively;
And filtering the dynamic target in the second partial point cloud map to obtain a third partial point cloud map.
Optionally, the step of determining the dynamic target based on the point cloud information in the first local point cloud map acquired twice successively includes:
Acquiring environment point cloud information again to generate a first local point cloud map after a preset time interval, and filtering static targets in the first local point cloud map to obtain a fourth local point cloud map;
determining position change information of the unmanned vehicle when the first local point cloud map is acquired twice successively;
Updating the point cloud information of each object contained in the fourth local point cloud map according to the position change information to obtain a fifth local point cloud map;
Comparing the fifth partial point cloud map with the second partial point cloud map, and determining an object with the point cloud information variation outside a preset range as a dynamic target.
In a second aspect, an embodiment of the present application provides a point cloud map construction apparatus, including:
The acquisition module is used for acquiring environment point cloud information to generate a first local point cloud map in the process that the unmanned vehicle moves according to a preset route;
The first filtering module is used for filtering a preset static target in the first local point cloud map to obtain a second local point cloud map;
the second filtering module is used for filtering the dynamic target in the second local point cloud map to obtain a third local point cloud map;
The construction module is used for constructing a target point cloud map based on each third local point cloud map generated in the process that the unmanned vehicle moves according to the preset route.
Optionally, the acquisition module includes:
The first sub-module is used for determining the distance between the laser radar installed on the unmanned vehicle and each object in the surrounding environment based on the time interval of the transmitted and received pulse signals and taking the distance as the acquired environment point cloud information in the process that the unmanned vehicle moves according to the preset route;
And the second sub-module is used for generating a first local point cloud map based on the collected environmental point cloud information, wherein the first local point cloud map is used for representing the environmental information around the unmanned vehicle at a certain moment.
Optionally, the first filtering module includes:
A third sub-module, configured to perform target segmentation processing on the first local point cloud map to obtain each object included in the first local point cloud map, and then perform color marking processing on each object, where each object corresponds to one point cloud information, each point cloud information corresponds to one distance value, and color marking is performed on each object included in the first local point cloud map based on a preset corresponding relationship between colors and distances when color marking is performed;
and the fourth sub-module is used for filtering preset static targets in the objects after color marking to obtain the second local point cloud map.
Optionally, the second filtering module includes:
a fifth sub-module, configured to determine a dynamic target based on point cloud information in a first local point cloud map acquired twice in succession;
and the sixth sub-module is used for filtering the dynamic target in the second local point cloud map to obtain a third local point cloud map.
Optionally, the fifth submodule includes:
the acquisition unit is used for acquiring the environmental point cloud information again to generate a first local point cloud map after a preset time interval, filtering out a static target in the first local point cloud map and obtaining a fourth local point cloud map;
the position change information determining unit is used for determining position change information of the unmanned vehicle when the first local point cloud map is acquired twice successively;
the updating unit is used for updating the point cloud information of each object contained in the fourth local point cloud map according to the position change information to obtain a fifth local point cloud map;
and the comparison unit is used for comparing the fifth local point cloud map with the second local point cloud map and determining an object with the point cloud information variation outside a preset range as a dynamic target.
In a third aspect, an embodiment of the present application provides an electronic device, including a processor, a memory, and a program or instruction stored on the memory and executable on the processor, the program or instruction implementing the steps of the method according to the first aspect when executed by the processor.
In a fourth aspect, embodiments of the present application provide a readable storage medium having stored thereon a program or instructions which when executed by a processor perform the steps of the method according to the first aspect.
By adopting the technical scheme, compared with the prior art, the application has the technical effects that:
The point cloud map construction method includes the steps of collecting environmental point cloud information to generate a first local point cloud map in the moving process of an unmanned vehicle according to a preset route, filtering preset static targets in the first local point cloud map to obtain a second local point cloud map, filtering dynamic targets in the second local point cloud map to obtain a third local point cloud map, and constructing a target point cloud map based on each third local point cloud map generated in the moving process of the unmanned vehicle according to the preset route. According to the scheme, the dynamic targets and the static targets with smaller reference values in the point cloud map can be filtered, so that the high-quality point cloud map is obtained, and more accurate position information is obtained in the subsequent positioning process. Specifically, on one hand, the interference positioning of the dynamic target is avoided, so that the accuracy of a positioning result is improved, and on the other hand, the increase of the calculated amount of the static target with smaller reference value is avoided, and the positioning speed is improved.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that are needed in the description of the embodiments of the present invention will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart showing the steps of a point cloud map construction method according to an embodiment of the present application;
FIG. 2 is a schematic diagram showing the correspondence between distance and color according to an embodiment of the present application;
Fig. 3 is a block diagram showing a construction of a point cloud map construction apparatus according to an embodiment of the present application;
FIG. 4 is a block diagram showing the structure of an electronic device according to an embodiment of the present application;
Fig. 5 is a block diagram showing the structure of still another electronic device of the embodiment of the present application.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present application more apparent, the technical solutions of the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application, and it is apparent that the described embodiments are some embodiments of the present application, but not all embodiments of the present application. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
The device detection method provided by the embodiment of the application is described in detail below through specific embodiments and application scenes thereof with reference to the accompanying drawings.
Referring to fig. 1, a flowchart of steps of a point cloud map construction method according to an embodiment of the present application is shown.
The point cloud map construction method of the embodiment of the application comprises the following steps:
And 101, acquiring environmental point cloud information to generate a first local point cloud map in the process that the unmanned vehicle moves according to a preset route.
In the actual implementation process, a laser radar can be arranged on the unmanned vehicle, and information acquisition in the point cloud map construction process is carried out through the laser radar. The acquisition mode comprises the steps of determining the distance between a laser radar installed on the unmanned vehicle and each object in the surrounding environment based on the time interval of a transmitted and received pulse signal in the moving process of the unmanned vehicle according to a preset route to serve as acquired environment point cloud information, and generating a first local point cloud map based on the acquired environment point cloud information, wherein the first local point cloud map is used for representing the environment information around the unmanned vehicle at a certain moment. And determining each object contained in the surrounding environment and the distance between each object and the laser radar when the unmanned vehicle is collected through the point cloud information in the first local point cloud map.
The preset time interval may be flexibly set by those skilled in the art, and is not particularly limited in the present application. The laser radar is in direct proportion to the distance between the laser radar and the target object according to the time interval between the pulse signal transmitted by the transmitter and the reflected pulse signal received by the receiver.
In the embodiment of the application, the laser radar arranged on the unmanned vehicle is selected to collect information in the process of constructing the point cloud map, and the cost of the laser radar is low, so that the cost of the unmanned vehicle can be reduced, and the accuracy of the data collected by the laser radar is high.
Step 102, filtering out a preset static target in the first partial point cloud map to obtain a second partial point cloud map.
The point cloud map construction method provided by the embodiment of the application aims at the problem that the positioning accuracy is low in the subsequent positioning process when the surrounding environment is dynamic when the unmanned vehicle builds the point cloud map, if a dynamic target exists, the solution is determined through creative labor and comprehensive factors, and the dynamic target in the environment is eliminated in the construction of the point cloud map so as to ensure the positioning accuracy. Besides, besides the dynamic targets, a part of static targets, such as the ground, exist in the point cloud map, the part of static targets provide less effective information for the positioning process, and the calculation amount of the positioning process is large, so that the instantaneity of the positioning process is affected. Therefore, in the embodiment of the application, when the point cloud map is constructed, the static target with smaller reference value in the environment, namely the preset static target, is eliminated, so that the calculation amount consumed by positioning is reduced.
And after filtering out the preset static targets in the first local point cloud map, identifying the dynamic targets in the point cloud map, and filtering out the identified dynamic targets. The feasibility mode comprises the steps of determining a dynamic target based on point cloud information in a first local point cloud map acquired twice successively, and filtering the dynamic target in a second local point cloud map to obtain a third local point cloud map.
In an actual implementation process, the laser radar installed on the unmanned vehicle performs primary point cloud information at preset intervals, and the processor generates a first local point cloud map based on the point cloud information acquired by the laser radar. That is, in the whole process that the unmanned vehicle moves on the preset route, the processor generates N first local point cloud maps when N times of point cloud information is acquired.
When determining a dynamic target contained in a local point cloud map, the dynamic target and the dynamic target are determined by combining a first local point cloud map generated immediately after the dynamic target and the dynamic target.
And 103, filtering out a dynamic target in the second local point cloud map to obtain a third local point cloud map.
In the process of constructing the point cloud map, aiming at each second local point cloud map, the dynamic targets in the environment are eliminated, so that the interference positioning of the dynamic targets can be effectively avoided, and the accuracy of the positioning result obtained in the subsequent positioning based on the constructed point cloud map can be improved.
It should be noted that, steps 101 to 103 are a process of filtering dynamic targets and static targets of a single first partial point cloud map, and filtering to obtain a third partial point cloud map. In the actual implementation process, the above procedure needs to be repeatedly executed to process each first local point cloud map generated in the running process of the unmanned vehicle according to the preset route, so as to obtain a corresponding third local point cloud map.
And 104, constructing a target point cloud map based on each third local point cloud map generated in the process that the unmanned vehicle moves according to the preset route.
When the target point cloud map is built based on each third local point cloud map, any suitable map synthesis mode may be adopted, and the method is not particularly limited in the embodiment of the present application. For example, a simple splicing mode, a mode of integrating the same point cloud information after investigation and the like can be adopted.
The point cloud map construction method includes the steps of collecting environmental point cloud information to generate a first local point cloud map in the moving process of an unmanned vehicle according to a preset route, filtering preset static targets in the first local point cloud map to obtain a second local point cloud map, filtering dynamic targets in the second local point cloud map to obtain a third local point cloud map, and constructing a target point cloud map based on each third local point cloud map generated in the moving process of the unmanned vehicle according to the preset route. According to the scheme, the dynamic target and the static target with smaller reference value in the point cloud map, namely the preset static target, can be filtered, so that the high-quality point cloud map is obtained, and more accurate position information can be obtained in the subsequent positioning process. Specifically, on one hand, the interference positioning of the dynamic target is avoided, so that the accuracy of a positioning result is improved, and on the other hand, the increase of the calculated amount of the static target with smaller reference value is avoided, and the positioning speed is improved.
In an alternative embodiment, the method for filtering the preset static target in the first partial point cloud map to obtain the second partial point cloud map includes the following steps:
Performing target segmentation processing on a first local point cloud map to obtain each object contained in the first local point cloud map, and performing color marking processing on each object;
wherein each object in the first local point cloud map may also be referred to as an environmental object, including a static object and a dynamic object. The objects in the first local point cloud map may include, but are not limited to, vehicles, pedestrians, bicycles, sign posts, light poles, and the ground.
Each object corresponds to a plurality of point cloud information, each point cloud information corresponds to a distance value, and each object contained in the first partial point cloud map is labeled based on a preset corresponding relation between colors and distances when the color is labeled. As in the first collected local point cloud map, each vehicle can be considered as an object, the vehicle corresponds to hundreds or thousands of laser points, each point has a distance value, and the hundreds or thousands of laser points form a laser cluster to represent the vehicle.
The distance-color correspondence is schematically shown in fig. 2, and different distance ranges correspond to different colors. The distance and color correspondence may be regarded as a distance and color control information matrix. In the application, the corresponding relation between the distance and the color is predefined and is used for marking the object contained in the local point cloud map. It should be noted that, the specific correspondence between the distance and the color may be flexibly set by those skilled in the art, which is not particularly limited in the embodiment of the present application. For example, the color is set to 2cm for dark red, 4cm for light red, and 6cm for blue.
And step two, filtering preset static targets in the objects after color marking to obtain a second local point cloud map.
The preset static target is a static object with smaller positioning reference value, such as a ground point. Because the reference value of the preset static target is smaller in positioning, the preset static target is filtered out.
The method for filtering the preset static target after the object is optionally subjected to color marking is small in calculated amount and accurate in filtering result.
In an alternative embodiment, the method for determining the dynamic target based on the point cloud information in the first local point cloud map acquired twice successively comprises the following steps:
Acquiring environment point cloud information again to generate a first local point cloud map after a preset time interval, and filtering static targets in the first local point cloud map to obtain a fourth local point cloud map;
the preset duration may be flexibly set by those skilled in the art, and is not particularly limited in the embodiment of the present application.
For example, if the first local point cloud map generated by the acquired environmental point cloud information in step 101 is a, the environmental point cloud information acquisition time point 1 of the first local point cloud map B in the step is spaced from the environmental point cloud information acquisition time point 2 of a by a preset time period, and no environmental point cloud information is acquired between the time point 1 and the time point 2.
Determining position change information of the unmanned vehicle when the first local point cloud map is acquired twice successively;
step three, updating the point cloud information of each object contained in the fourth local point cloud map according to the position change information to obtain a fifth local point cloud map;
when the point cloud information of each object included in the fourth partial point cloud map is updated according to the position change information, a coordinate value corresponding to the point cloud information and a coordinate value indicated by the position change information may be differenced for each point cloud information.
And step four, comparing the fifth local point cloud map with the second local point cloud map, and determining an object with the point cloud information variation outside a preset range as a dynamic target.
Within the preset range, the object is represented as a static target, and outside the preset range, the object is represented as a dynamic target.
The second local point cloud map is the point cloud information which is acquired at the moment t1 and is subjected to preprocessing, the fourth local point cloud map is the point cloud information which is acquired at the moment t2 and is subjected to preprocessing, the preset time length is t2-t1, and the position change information represents the position change condition of the unmanned vehicle in the preset time length.
If the target object is a static target, the position information of the target object at the time t1 and the time t2 is fixed, and the point cloud information reflects the distance between the unmanned vehicle and the target object, so theoretically, the second local point cloud map=the fourth local point cloud map-position conversion information, but considering the existence of an error, as long as the error between the second local point cloud map and the fourth local point cloud map-position conversion information is within a preset range, the object is considered to be a static target, otherwise, the object is considered to be a dynamic target.
More preferably, the moving direction and moving speed of the dynamic object can be determined according to the fifth local point cloud map and the second local point cloud map, and the moving direction and moving speed can be used as a reference factor in the determination of the dynamic object in the subsequent local point cloud map.
The method can be used for identifying the dynamic target by combining the position transformation information of the unmanned vehicle, and the accuracy of the identification result is high.
Fig. 3 is a block diagram of a point cloud map construction device according to an embodiment of the present application.
The point cloud map construction device of the embodiment of the application comprises the following modules:
the acquisition module 301 is configured to acquire environmental point cloud information to generate a first local point cloud map during a movement process of the unmanned vehicle according to a preset route;
The first filtering module 302 is configured to filter a preset static target in the first local point cloud map to obtain a second local point cloud map;
The second filtering module 303 is configured to filter out a dynamic target in the second local point cloud map to obtain a third local point cloud map;
the construction module 304 is configured to construct a target point cloud map based on each third local point cloud map generated in the movement process of the unmanned vehicle according to the preset route.
Optionally, the acquisition module includes:
The first sub-module is used for determining the distance between the laser radar on the unmanned vehicle and each object in the surrounding environment based on the time interval of the transmitted and received pulse signals in the process that the unmanned vehicle moves according to the preset route, and the distance is used as acquired environment point cloud information;
And the second sub-module is used for generating a first local point cloud map based on the collected environmental point cloud information, wherein the first local point cloud map is used for representing the environmental information around the unmanned vehicle at a certain moment.
Optionally, the first filtering module includes:
The third sub-module is used for carrying out target segmentation processing on the first local point cloud map to obtain objects contained in the first local point cloud map, and then carrying out color marking processing on the objects, wherein each object corresponds to a plurality of point cloud information, each point cloud information corresponds to a distance value, and each object contained in the first local point cloud map is subjected to color marking based on a preset corresponding relation between colors and distances when color marking is carried out;
And the fourth sub-module is used for filtering preset static targets in the objects after color marking to obtain a second local point cloud map.
Optionally, the second filtering module includes:
a fifth sub-module, configured to determine a dynamic target based on point cloud information in a first local point cloud map acquired twice in succession;
And the sixth sub-module is used for filtering the dynamic target in the second local point cloud map to obtain a third local point cloud map.
Optionally, the fifth submodule includes:
the acquisition unit is used for acquiring the environmental point cloud information again to generate a first local point cloud map after a preset time interval, filtering out a static target in the first local point cloud map and obtaining a fourth local point cloud map;
The position change information determining unit is used for determining position change information of the unmanned vehicle when the first local point cloud map is acquired twice successively;
the updating unit is used for updating the point cloud information of each object contained in the fourth local point cloud map according to the position change information to obtain a fifth local point cloud map;
The comparison unit is used for comparing the fifth local point cloud map with the second local point cloud map, and determining an object with the point cloud information variation outside a preset range as a dynamic target.
The point cloud map construction device provided by the embodiment of the application can filter the dynamic target and the static target with smaller reference value in the point cloud map, namely the preset static target, so that the point cloud map with high quality is obtained, and more accurate position information can be obtained in the subsequent positioning. Specifically, on one hand, the interference positioning of the dynamic target is avoided, so that the accuracy of a positioning result is improved, and on the other hand, the increase of the calculated amount of the static target with smaller reference value is avoided, and the positioning speed is improved.
The point cloud map construction device shown in fig. 2 in the embodiment of the present application may be a device, or may be a component, an integrated circuit, or a chip in a server. The point cloud map construction device shown in fig. 2 in the embodiment of the present application may be a device with an operating system. The operating system may be an Android operating system, an iOS operating system, or other possible operating systems, and the embodiment of the present application is not limited specifically.
The point cloud map construction device shown in fig. 3 provided by the embodiment of the present application can implement each process implemented by the method embodiment of fig. 1, and in order to avoid repetition, a detailed description is omitted here.
Optionally, as shown in fig. 4, the embodiment of the present application further provides an electronic device 400, including a processor 401, a memory 402, and a program or an instruction stored in the memory 402 and capable of running on the processor 401, where the program or the instruction implements each process of the embodiment of the point cloud map construction method when executed by the processor 401, and the process can achieve the same technical effect, so that repetition is avoided, and no further description is given here.
It should be noted that, the electronic device in the embodiment of the present application includes the server described above.
Fig. 5 is a schematic hardware structure of an electronic device implementing an embodiment of the present application.
The electronic device 500 includes, but is not limited to, a radio frequency unit 501, a network module 502, an audio output unit 503, an input unit 504, a sensor 505, a display unit 506, a user input unit 507, an interface unit 508, a memory 509, and a processor 510. Those skilled in the art will appreciate that the electronic device 500 may further include a power source (e.g., a battery) for powering the various components, and that the power source may be logically coupled to the processor 510 via a power management system to perform functions such as managing charging, discharging, and power consumption via the power management system. The electronic device structure shown in fig. 5 does not constitute a limitation of the electronic device, and the electronic device may include more or less components than shown, or may combine certain components, or may be arranged in different components, which are not described in detail herein.
The processor 510 is configured to execute the flow of the point cloud map construction method.
It should be appreciated that in embodiments of the present application, the input unit 504 may include a graphics processor (Graphics Processing Unit, GPU) 5041 and a microphone 5042, with the graphics processor 5041 processing image data of still pictures or video obtained by an image capture device (e.g., a camera) in a video capture mode or an image capture mode. The display unit 506 may include a display panel 5061, and the display panel 5061 may be configured in the form of a liquid crystal display, an organic light emitting diode, or the like. The user input unit 507 includes a touch panel 5071 and other input devices 5072. Touch panel 5071, also referred to as a touch screen. Touch panel 5071 may include two parts, a touch detection device and a touch controller. Other input devices 5072 may include, but are not limited to, a physical keyboard, function keys (e.g., volume control keys, switch keys, etc.), a trackball, a mouse, a joystick, and so forth, which are not described in detail herein. The memory 509 may be used to store software programs as well as various data including, but not limited to, application programs and an operating system. Processor 510 may integrate an application processor that primarily handles operating systems, user interfaces, applications, etc., with a modem processor that primarily handles wireless communications. It will be appreciated that the modem processor described above may not be integrated into the processor 510.
The embodiment of the application also provides a readable storage medium, wherein the readable storage medium stores a program or an instruction, and the program or the instruction realizes each process of the embodiment of the point cloud map construction method when being executed by a processor, and can achieve the same technical effect, so that repetition is avoided and redundant description is omitted.
Wherein the processor is a processor in the electronic device described in the above embodiment. The readable storage medium includes a computer readable storage medium such as a Read-Only Memory (ROM), a random access Memory (Random Access Memory, RAM), a magnetic disk or an optical disk, and the like.
The embodiment of the application further provides a chip, which comprises a processor and a communication interface, wherein the communication interface is coupled with the processor, and the processor is used for running programs or instructions to realize the processes of the embodiment of the point cloud map construction method, and the same technical effects can be achieved, so that repetition is avoided, and the description is omitted here.
It should be understood that the chips referred to in the embodiments of the present application may also be referred to as system-on-chip chips, chip systems, or system-on-chip chips, etc.
It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element. Furthermore, it should be noted that the scope of the methods and apparatus in the embodiments of the present application is not limited to performing the functions in the order shown or discussed, but may include performing the functions in a substantially simultaneous manner or in an opposite order depending on the functions involved, e.g., the described methods may be performed in an order different from that described, and various steps may be added, omitted, or combined. Additionally, features described with reference to certain examples may be combined in other examples.
From the above description of the embodiments, it will be clear to those skilled in the art that the above-described embodiment method may be implemented by means of software plus a necessary general hardware platform, but of course may also be implemented by means of hardware, but in many cases the former is a preferred embodiment. Based on such understanding, the technical solution of the present application may be embodied essentially or in a part contributing to the prior art in the form of a computer software product stored in a storage medium (e.g. ROM/RAM, magnetic disk, optical disk) comprising instructions for causing a terminal (which may be a mobile phone, a computer, a server, or a network device, etc.) to perform the method according to the embodiments of the present application.
The embodiments of the present application have been described above with reference to the accompanying drawings, but the present application is not limited to the above-described embodiments, which are merely illustrative and not restrictive, and many forms may be made by those having ordinary skill in the art without departing from the spirit of the present application and the scope of the claims, which are to be protected by the present application.
Claims (7)
1. A method of point cloud map construction, the method comprising:
Acquiring environmental point cloud information to generate a first local point cloud map in the moving process of the unmanned vehicle according to a preset route;
Filtering a preset static target in the first partial point cloud map to obtain a second partial point cloud map;
Filtering out a dynamic target in the second partial point cloud map to obtain a third partial point cloud map;
constructing a target point cloud map based on each third local point cloud map generated in the process that the unmanned vehicle moves according to the preset route;
Filtering the dynamic target in the second partial point cloud map to obtain a third partial point cloud map, including:
determining a dynamic target based on point cloud information in a first local point cloud map acquired twice successively;
Filtering out a dynamic target in the second partial point cloud map to obtain a third partial point cloud map;
The step of determining the dynamic target based on the point cloud information in the first local point cloud map acquired twice successively comprises the following steps:
Acquiring environment point cloud information again to generate a first local point cloud map after a preset time interval, and filtering static targets in the first local point cloud map to obtain a fourth local point cloud map;
determining position change information of the unmanned vehicle when the first local point cloud map is acquired twice successively;
Updating the point cloud information of each object contained in the fourth local point cloud map according to the position change information to obtain a fifth local point cloud map;
Comparing the fifth partial point cloud map with the second partial point cloud map, and determining an object with the point cloud information variation outside a preset range as a dynamic target.
2. The method of claim 1, wherein the step of collecting ambient point cloud information to generate the first local point cloud map during movement of the unmanned vehicle along the predetermined route comprises:
In the moving process of the unmanned vehicle according to a preset route, a laser radar installed on the unmanned vehicle determines the distance between the laser radar and each object in the surrounding environment based on the time interval of the transmitted and received pulse signals, and the distance is used as acquired environment point cloud information;
Generating a first local point cloud map based on the collected environmental point cloud information, wherein the first local point cloud map is used for representing environmental information around the unmanned vehicle at a certain moment.
3. The method of claim 1, wherein the step of filtering the preset static target in the first partial point cloud map to obtain a second partial point cloud map comprises:
Performing target segmentation processing on the first partial point cloud map to obtain objects contained in the first partial point cloud map, and performing color marking processing on the objects, wherein each object corresponds to a plurality of point cloud information, each point cloud information corresponds to a distance value, and color marking is performed on the objects contained in the first partial point cloud map based on a preset corresponding relation between colors and distances when color marking is performed;
And filtering out preset static targets in the objects after color marking to obtain a second local point cloud map.
4. A point cloud map construction apparatus, comprising:
The acquisition module is used for acquiring environment point cloud information to generate a first local point cloud map in the process that the unmanned vehicle moves according to a preset route;
The first filtering module is used for filtering a preset static target in the first local point cloud map to obtain a second local point cloud map;
the second filtering module is used for filtering the dynamic target in the second local point cloud map to obtain a third local point cloud map;
the construction module is used for constructing a target point cloud map based on each third local point cloud map generated in the process that the unmanned vehicle moves according to the preset route;
The second filtering module comprises:
a fifth sub-module, configured to determine a dynamic target based on point cloud information in a first local point cloud map acquired twice in succession;
A sixth sub-module, configured to filter out a dynamic target in the second local point cloud map, to obtain a third local point cloud map;
the fifth sub-module includes:
the acquisition unit is used for acquiring the environmental point cloud information again to generate a first local point cloud map after a preset time interval, filtering out a static target in the first local point cloud map and obtaining a fourth local point cloud map;
the position change information determining unit is used for determining position change information of the unmanned vehicle when the first local point cloud map is acquired twice successively;
the updating unit is used for updating the point cloud information of each object contained in the fourth local point cloud map according to the position change information to obtain a fifth local point cloud map;
and the comparison unit is used for comparing the fifth local point cloud map with the second local point cloud map and determining an object with the point cloud information variation outside a preset range as a dynamic target.
5. The apparatus of claim 4, wherein the acquisition module comprises:
The first sub-module is used for determining the distance between the laser radar installed on the unmanned vehicle and each object in the surrounding environment based on the time interval of the transmitted and received pulse signals and taking the distance as the acquired environment point cloud information in the process that the unmanned vehicle moves according to the preset route;
And the second sub-module is used for generating a first local point cloud map based on the collected environmental point cloud information, wherein the first local point cloud map is used for representing the environmental information around the unmanned vehicle at a certain moment.
6. The apparatus of claim 4, wherein the first filtering module comprises:
A third sub-module, configured to perform target segmentation processing on the first local point cloud map to obtain each object included in the first local point cloud map, and then perform color marking processing on each object, where each object corresponds to a plurality of point cloud information, each point cloud information corresponds to a distance value, and color marking is performed on each object included in the first local point cloud map based on a preset corresponding relationship between color and distance when color marking is performed;
And the fourth sub-module is used for filtering preset static targets in the objects after color marking to obtain a second local point cloud map.
7. An electronic device comprising a processor, a memory and a program or instruction stored on the memory and executable on the processor, which when executed by the processor, implements the steps of the point cloud mapping method of any of claims 1-3.
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