CN113359148B - Laser radar point cloud data processing method, device, equipment and storage medium - Google Patents

Laser radar point cloud data processing method, device, equipment and storage medium Download PDF

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Publication number
CN113359148B
CN113359148B CN202010104936.4A CN202010104936A CN113359148B CN 113359148 B CN113359148 B CN 113359148B CN 202010104936 A CN202010104936 A CN 202010104936A CN 113359148 B CN113359148 B CN 113359148B
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point cloud
cloud data
grid
laser radar
target
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CN113359148A (en
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卢飞翔
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Beijing Baidu Netcom Science and Technology Co Ltd
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Beijing Baidu Netcom Science and Technology Co Ltd
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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
    • E—FIXED CONSTRUCTIONS
    • E02—HYDRAULIC ENGINEERING; FOUNDATIONS; SOIL SHIFTING
    • E02F—DREDGING; SOIL-SHIFTING
    • E02F5/00—Dredgers or soil-shifting machines for special purposes
    • E02F5/02—Dredgers or soil-shifting machines for special purposes for digging trenches or ditches
    • E02F5/14—Component parts for trench excavators, e.g. indicating devices travelling gear chassis, supports, skids
    • E02F5/145—Component parts for trench excavators, e.g. indicating devices travelling gear chassis, supports, skids control and indicating devices
    • E—FIXED CONSTRUCTIONS
    • E02—HYDRAULIC ENGINEERING; FOUNDATIONS; SOIL SHIFTING
    • E02F—DREDGING; SOIL-SHIFTING
    • E02F9/00—Component parts of dredgers or soil-shifting machines, not restricted to one of the kinds covered by groups E02F3/00 - E02F7/00
    • E02F9/26—Indicating devices
    • E02F9/261—Surveying the work-site to be treated
    • 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
    • G01S17/02—Systems using the reflection of electromagnetic waves other than radio waves
    • 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
    • G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
    • G01S7/48—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S17/00
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
    • Y02A90/00—Technologies having an indirect contribution to adaptation to climate change
    • Y02A90/10—Information and communication technologies [ICT] supporting adaptation to climate change, e.g. for weather forecasting or climate simulation

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  • Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Electromagnetism (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Mining & Mineral Resources (AREA)
  • Radar, Positioning & Navigation (AREA)
  • Remote Sensing (AREA)
  • Civil Engineering (AREA)
  • General Engineering & Computer Science (AREA)
  • Structural Engineering (AREA)
  • Mechanical Engineering (AREA)
  • Optical Radar Systems And Details Thereof (AREA)
  • Traffic Control Systems (AREA)

Abstract

The application discloses a laser radar point cloud data processing method, device, equipment and storage medium, and relates to the technical field of intelligent excavators. The specific implementation scheme is as follows: the depth value and the confidence coefficient of each grid in the grid map are determined, and the corresponding confidence coefficient map is obtained, so that the aim of automatically enhancing the target laser radar point cloud data of the target soil ditch is fulfilled, a sensing device with higher configuration is not required to be arranged for the intelligent excavator, the cost of the intelligent excavator is saved, and the control device of the intelligent excavator can accurately determine the excavating information of the next shovel of the intelligent excavator according to the confidence coefficient map.

Description

Laser radar point cloud data processing method, device, equipment and storage medium
Technical Field
The application relates to the technical field of data processing, in particular to an intelligent excavator technology.
Background
With the development of artificial intelligence technology, intelligent excavators are also receiving more and more attention. In a trenching task, an intelligent excavator needs to obtain a three-dimensional shape of a soil ditch before each digging so as to determine the digging position and/or the digging action of a next shovel.
In the prior art, a laser radar of an intelligent excavator is used as a three-dimensional sensing hardware device, but the acquired data is very sparse in the vertical direction, so that the requirement of excavation cannot be met, and the intelligent excavator cannot accurately determine the excavation position and/or the excavation action of the next shovel.
Disclosure of Invention
The embodiment of the application provides a laser radar point cloud data processing method, device, equipment and storage medium, which are used for solving the technical problem that in the prior art, the intelligent excavator cannot accurately determine the excavation information of the next shovel because the data acquired by the laser radar of the intelligent excavator cannot meet the excavation requirement.
An embodiment of the present application provides a method for processing laser radar point cloud data, including:
Acquiring target laser radar point cloud data of a target soil ditch;
Generating a grid map corresponding to the target laser radar point cloud data;
and determining the depth value and the confidence coefficient of each grid in the grid map to obtain a corresponding confidence coefficient map, wherein the confidence coefficient map is used for determining the digging information of the next shovel of the intelligent excavator.
According to the embodiment of the application, the target laser radar point cloud data of the target soil ditch are obtained, the grid map corresponding to the target laser radar point cloud data is generated, then the depth value and the confidence degree of each grid in the grid map are determined, and the corresponding confidence degree map is obtained, so that the aim of automatically enhancing the target laser radar point cloud data of the target soil ditch is fulfilled, and the control equipment of the intelligent excavator can accurately determine the excavating information of the next shovel of the intelligent excavator according to the confidence degree map.
Optionally, the determining the depth value of each grid in the grid map includes:
And determining the depth value of each grid in the grid map according to the distances between K point cloud data closest to the grid in the target laser radar point cloud data and the grid, wherein K is an integer greater than 3.
Optionally, the determining the depth value of the grid according to the distances between K closest point cloud data to the grid in the target lidar point cloud data and the grid includes:
determining normalized weights of K point cloud data according to the distances between K point cloud data closest to the grid in the point cloud data of the target laser radar and the grid;
And carrying out weighting processing on the depth values of the K point cloud data according to the normalized weights of the K point cloud data to obtain the depth values of the grid.
Optionally, determining the confidence level of each grid in the grid map includes:
And determining the confidence level of each grid in the grid map according to the distances between the L nearest point cloud data to the grid in the target laser radar point cloud data and the grid, wherein L is an integer greater than 1.
Optionally, the determining the confidence level of the grid according to the distances between the L closest point cloud data to the grid in the target lidar point cloud data and the grid includes:
Determining the average distance between the grid and the L point cloud data according to the distances between the L point cloud data closest to the grid in the target laser radar point cloud data and the grid;
And determining the confidence level of the grid according to the average distance between the grid and the L point cloud data, the average distance upper limit threshold value and the average distance lower limit threshold value of the grid map.
Optionally, the generating the grid map corresponding to the target lidar point cloud data includes:
and generating a grid map corresponding to the target laser radar point cloud data according to the size of the target soil ditch and the preset grid cell size.
Optionally, the acquiring target lidar point cloud data of the target soil ditch includes:
acquiring at least one frame of laser radar point cloud data of a target soil ditch;
and carrying out data preprocessing on the at least one frame of laser radar point cloud data to obtain the target laser radar point cloud data.
Optionally, the performing data preprocessing on the at least one frame of lidar point cloud data to obtain the target lidar point cloud data includes:
Fitting the at least one frame of laser radar point cloud data according to the rotation degree corresponding to each frame of the obtained laser radar point cloud data to obtain primary laser radar point cloud data;
And registering the primary laser radar point cloud data to obtain the target laser radar point cloud data.
A second aspect of an embodiment of the present application provides a laser radar point cloud data processing apparatus, including:
the acquisition module is used for acquiring target laser radar point cloud data of the target soil ditch;
The generation module is used for generating a grid map corresponding to the target laser radar point cloud data;
the determining module is used for determining the depth value and the confidence coefficient of each grid in the grid map to obtain a corresponding confidence coefficient map, wherein the confidence coefficient map is used for determining the digging information of the next shovel of the intelligent excavator.
Optionally, the determining module includes:
The first determining unit is used for determining the depth value of each grid in the grid map according to the distances between K point cloud data closest to the grid in the target laser radar point cloud data and the grid, wherein K is an integer greater than 3.
Optionally, the first determining unit is specifically configured to:
determining normalized weights of K point cloud data according to the distances between K point cloud data closest to the grid in the point cloud data of the target laser radar and the grid;
And carrying out weighting processing on the depth values of the K point cloud data according to the normalized weights of the K point cloud data to obtain the depth values of the grid.
Optionally, the determining module includes:
the second determining unit is configured to determine, for each grid in the grid map, a confidence level of the grid according to distances between L point cloud data closest to the grid in the target lidar point cloud data and the grid, where L is an integer greater than 1.
Optionally, the second determining unit is specifically configured to:
Determining the average distance between the grid and the L point cloud data according to the distances between the L point cloud data closest to the grid in the target laser radar point cloud data and the grid;
And determining the confidence level of the grid according to the average distance between the grid and the L point cloud data, the average distance upper limit threshold value and the average distance lower limit threshold value of the grid map.
Optionally, the generating module is specifically configured to:
and generating a grid map corresponding to the target laser radar point cloud data according to the size of the target soil ditch and the preset grid cell size.
Optionally, the acquiring module includes:
The acquisition unit is used for acquiring at least one frame of laser radar point cloud data of the target soil ditch;
and the processing unit is used for carrying out data preprocessing on the at least one frame of laser radar point cloud data to obtain the target laser radar point cloud data.
Optionally, the processing unit is specifically configured to:
Fitting the at least one frame of laser radar point cloud data according to the rotation degree corresponding to each frame of the obtained laser radar point cloud data to obtain primary laser radar point cloud data;
And registering the primary laser radar point cloud data to obtain the target laser radar point cloud data.
A third aspect of an embodiment of the present application provides an electronic device, including:
At least one processor; and a memory communicatively coupled to the at least one processor;
wherein the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of any one of the first aspects described above.
A fourth aspect of the embodiments of the present application provides a non-transitory computer-readable storage medium storing computer instructions for causing a computer to perform the method according to any one of the first aspects above.
According to a fifth aspect of the present application, there is provided a computer program product comprising: a computer program stored in a readable storage medium, from which it can be read by at least one processor of an electronic device, the at least one processor executing the computer program causing the electronic device to perform the method of the first aspect.
In summary, the embodiment of the present application has the following beneficial effects compared with the prior art:
According to the laser radar point cloud data processing method, device and equipment and the storage medium, the target laser radar point cloud data of the target soil ditch are obtained, the grid map corresponding to the target laser radar point cloud data is generated, then the depth value and the confidence degree of each grid in the grid map are determined, and the corresponding confidence degree map is obtained, so that the purpose of automatic data enhancement of the target laser radar point cloud data of the target soil ditch is achieved, a sensing device with higher configuration is not required to be arranged for the intelligent excavator, the cost of the intelligent excavator is saved, and the control equipment of the intelligent excavator can accurately determine the excavating information of the next shovel of the intelligent excavator according to the confidence degree map.
Other effects of the above alternative will be described below in connection with specific embodiments.
Drawings
The drawings are included to provide a better understanding of the present application and are not to be construed as limiting the application. Wherein:
fig. 1 is a schematic diagram of an application scenario provided in an embodiment of the present application;
fig. 2 is a flow chart of a laser radar point cloud data processing method according to an embodiment of the application;
FIG. 3 is a schematic diagram of a multi-scale grid map according to an embodiment of the present application under different viewing angles;
Fig. 4 is a schematic structural diagram of a grid map according to an embodiment of the present application;
FIG. 5 is a schematic diagram of a confidence map according to an embodiment of the present application;
Fig. 6 is a flow chart of a laser radar point cloud data processing method according to another embodiment of the present application;
Fig. 7 is a schematic diagram of multi-frame laser radar point cloud data according to an embodiment of the present application;
Fig. 8 is a schematic fitting diagram of multi-frame laser radar point cloud data according to an embodiment of the present application;
fig. 9 is a schematic registration diagram of multi-frame laser radar point cloud data provided by an embodiment of the present application;
Fig. 10 is a schematic diagram of data processing of single-frame lidar point cloud data at different view angles according to an embodiment of the present application;
fig. 11 is a schematic diagram illustrating a comparison between a grid map corresponding to single-frame lidar point cloud data and a grid map corresponding to multi-frame lidar point cloud data at different viewing angles according to an embodiment of the present application;
fig. 12 is a schematic diagram of comparison between a confidence map corresponding to single-frame laser radar point cloud data and a confidence map corresponding to multi-frame laser radar point cloud data according to an embodiment of the present application;
Fig. 13 is a schematic structural diagram of a laser radar point cloud data processing device according to an embodiment of the present application;
fig. 14 is a block diagram of an electronic device for implementing a laser radar point cloud data processing method according to an embodiment of the present application.
Detailed Description
Exemplary embodiments of the present application will now be described with reference to the accompanying drawings, in which various details of the embodiments of the present application are included to facilitate understanding, and are to be considered merely exemplary. Accordingly, those of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope and spirit of the application. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.
First, an application scenario and related part of vocabulary of the embodiment of the present application will be explained.
The laser radar point cloud data processing method, the device, the equipment and the storage medium provided by the embodiment of the application can be applied to the electronic equipment running the intelligent mining application program or the three-dimensional point cloud processing application program, and can be applied to other application scenes. For easy understanding, the following embodiments of the present application will be described with reference to application scenarios of the embodiments of the present application as applied to an intelligent excavator.
Fig. 1 is a schematic diagram of an application scenario provided in an embodiment of the present application. As shown in fig. 1, the application scenario of the embodiment of the present application may include, but is not limited to: a sensing device 10 of the intelligent excavator and a control device 11 of the intelligent excavator.
Illustratively, the sensing device 10 of the intelligent excavator involved in the embodiment of the present application may include, but is not limited to, a lidar device.
Illustratively, the control device 11 of the intelligent excavator according to the embodiment of the present application may include, but is not limited to, a main control computer of the intelligent excavator.
As shown in fig. 1, the sensing device 10 of the intelligent excavator is configured to collect working environment data (such as at least one frame of laser radar point cloud data of a target soil ditch in the following embodiment of the present application) of the intelligent excavator during execution of an excavating operation, perform data processing on the collected working environment data (such as data preprocessing performed by an electronic device in the following embodiment of the present application, generating a grid map, a confidence map and the like) to obtain processed working environment data, and then send the processed working environment data to the control device 11 of the intelligent excavator, so that the control device 11 of the intelligent excavator determines excavating information of a next shovel of the intelligent excavator according to the processed working environment data.
It should be understood that the above-mentioned process of performing data processing on the collected operation environment data may also be performed by the control device 11 of the intelligent excavator, that is, the control device 11 of the intelligent excavator performs data processing (such as data preprocessing performed by the electronic device, generating a grid map, a confidence map, and the like, which are related to the embodiment of the present application) on the operation environment data collected by the sensing device 10 of the intelligent excavator to obtain processed operation environment data, and determines the excavation information of the next shovel of the intelligent excavator according to the processed operation environment data.
Aiming at the technical problems that data acquired by a laser radar of an intelligent excavator in the prior art are very sparse in the vertical direction and cannot meet the requirement of excavation, so that the intelligent excavator cannot accurately determine the excavation position and/or the excavation action of a next shovel, the laser radar point cloud data processing method, the device, the equipment and the storage medium provided by the embodiment of the application are beneficial to the intelligent excavator to accurately determine the excavation position and/or the excavation action of the next shovel by generating a grid map (grid map) corresponding to target laser radar point cloud data of the target soil ditch and determining the depth value and the confidence of each grid in the grid map, so that a confidence map (confidence map) for representing three-dimensional shape information of the target soil ditch in detail is obtained.
The target soil ditch related in the embodiment of the application refers to a soil ditch required by an intelligent excavator in the process of executing the excavating operation.
Any lidar point cloud data referred to in the embodiments of the present application refers to a set of vectors in a three-dimensional coordinate system. These vectors are typically expressed in the form of X, Y, Z three-dimensional coordinates and are generally primarily used to represent the shape of the exterior surface of an object, where the Z-axis direction is used to indicate depth or height values. Furthermore, in addition to the geometric position information represented by (X, Y, Z), the laser radar point cloud data may represent information of RGB color, gray value, depth, division result, and the like of one point.
The depth value of any grid related in the embodiment of the application is used for representing the depth value or the height value of the grid in the Z-axis direction.
The confidence level of any grid involved in the embodiment of the application is used for representing the numerical reliability of the grid. Illustratively, the higher the confidence of the grid, the better the reliability of the numerical value representing the grid.
The confidence map related to the embodiment of the application is used for representing the three-dimensional shape information of the target soil ditch in detail.
The technical scheme of the application is described in detail below by specific examples. The following embodiments may be combined with each other, and the same or similar concepts or processes may not be described in detail in some embodiments.
Fig. 2 is a flowchart of a laser radar point cloud data processing method according to an embodiment of the present application. The execution body of the embodiment of the present application may be the above-mentioned electronic device, or a laser radar point cloud data processing apparatus in the above-mentioned electronic device (for convenience of description, in this embodiment, the execution body is described as an example of the above-mentioned electronic device). The laser radar point cloud data processing device may be implemented by software and/or hardware. As shown in fig. 2, the laser radar point cloud data processing method provided in this embodiment may include:
And S201, acquiring target laser radar point cloud data of a target soil ditch.
The target soil ditch related in the embodiment of the application refers to a soil ditch required by an intelligent excavator in the process of executing the excavating operation.
In this step, the electronic device may acquire target lidar point cloud data of the target soil ditch, where the target lidar point cloud data may include original single-frame or multi-frame lidar point cloud data of the target soil ditch acquired by the sensing device 10 of the intelligent excavator, or may include single-frame or multi-frame point cloud data obtained by performing data preprocessing on the acquired original single-frame or multi-frame lidar point cloud data of the target soil ditch by the sensing device 10 of the intelligent excavator, where the data preprocessing may include, but is not limited to: fitting and/or registration processes.
It should be understood that, if the electronic device is the sensing device 10 of the intelligent excavator, the electronic device may acquire the target lidar point cloud data of the electronic device itself; if the electronic device is other than the sensing device 10 of the intelligent excavator, the electronic device may acquire the target lidar point cloud data from the sensing device 10 of the intelligent excavator.
Step S202, generating a grid map corresponding to the target laser radar point cloud data.
In the step, the electronic equipment generates a grid map corresponding to the target laser radar point cloud data so as to further determine the depth value and the confidence coefficient of each grid in the grid map to obtain a corresponding confidence coefficient map, thereby achieving the purpose of automatically enhancing the target laser radar point cloud data of the target soil ditch.
For example, the electronic device may generate the grid map corresponding to the target lidar point cloud data according to the size of the target soil ditch and a preset grid cell size.
Fig. 3 is a schematic diagram of a multi-scale grid map provided by the embodiment of the present application under different view angles, as shown in fig. 3, in this step, a multi-scale uniform grid map may be automatically generated according to the size requirement of the trenching, for example, a grid map of 0.05m x 0.05m at view angle 1 and view angle 2 in fig. 3- (a), a grid map of 0.1 x 0.1m at view angle 1 and view angle 2 in fig. 3- (b), and a grid map of 0.2 x 0.2m at view angle 1 and view angle 2 in fig. 3- (c), which may lay a foundation for determining the excavation position and planning the excavation action.
Step S203, determining the depth value and the confidence coefficient of each grid in the grid map to obtain a corresponding confidence coefficient map, wherein the confidence coefficient map is used for determining the digging information of the next shovel of the intelligent excavator.
In this step, the electronic device determines the depth value and the confidence coefficient of each grid in the grid map to obtain a confidence coefficient map for representing the three-dimensional shape information of the target soil ditch in detail, so as to achieve the purpose of automatically enhancing the target laser radar point cloud data of the target soil ditch, so that the control device of the intelligent excavator can accurately determine the excavating information of the next shovel of the intelligent excavator, such as the excavating position and/or the excavating action, according to the confidence coefficient map.
The following embodiments of the present application describe a manner of determining the depth value and confidence of each grid in turn.
1) Determination of depth values
For each grid in the grid map, the electronic device may determine a depth value of the grid according to distances between K point cloud data closest to the grid in the target lidar point cloud data and the grid, where K may be an integer greater than 3, for example, k=8.
In the embodiment of the application, the electronic device may determine the depth value of the grid based on the center point of the grid, that is, the electronic device may determine the depth value of the grid according to K pieces of point cloud data closest to the center point of the grid in the target lidar point cloud data.
Fig. 4 is a schematic structural diagram of a grid map according to an embodiment of the present application, as shown in fig. 4, where the electronic device may determine a depth value of the grid a according to K pieces of point cloud data closest to a center point O of the grid a on an X-Y plane in the target lidar point cloud data.
It should be understood that the above-mentioned electronic device may also determine the depth value of the grid based on other points of the grid, which is not limited in the embodiment of the present application.
Optionally, for each grid in the grid map, the electronic device may determine a normalized weight of the K point cloud data according to a distance between K point cloud data closest to the grid in the target lidar point cloud data and the grid, and then weight the depth values of the K point cloud data according to the normalized weight of the K point cloud data to obtain the depth value of the grid. It should be understood that, the distances between the K point cloud data and the grid in the embodiments of the present application may refer to the distances between the K point cloud data and the center point of the grid; of course, it may also refer to distances between the above-mentioned K point cloud data and other points of the grid.
For example, the electronic device may determine the normalized weights of the K point cloud data according to the following formula (a) according to the distances between the K point cloud data closest to the grid and the grid in the target lidar point cloud data.
Wherein w' k represents the normalized weight of the kth point cloud data in the order from the near to the far in the K point cloud data, w k represents the weight of the kth point cloud data, K may be an integer from 1 to K, w j represents the weight of the jth point cloud data in the order from the near to the far in the K point cloud data, w j＝(1-dj/dmax)2,dj represents the distance between the jth point cloud data and the grid, d max represents the preset distance upper threshold, and d max is greater than d K (e.g., d max may be equal to d K+1 between the (k+1) th point cloud data in the order from the near to the far in the target laser radar point cloud data and the grid).
Of course, the electronic device may determine the normalized weights of the K point cloud data according to other variations of the formula (a) or an equivalent formula, where the distance between the K point cloud data closest to the grid and the grid in the target lidar point cloud data is not limited in the embodiment of the present application.
Further, the electronic device may perform weighting processing on the depth values of the K point cloud data according to the following formula (two) according to the normalized weights of the K point cloud data, to obtain the depth value of the grid.
Wherein Z represents the depth value of the grid, and Z k represents the depth value of the kth point cloud data.
Of course, the electronic device may further perform weighting processing on the depth values of the K point cloud data according to other variants or equivalent formulas of the formula (two) according to the normalized weights of the K point cloud data to obtain the depth value of the grid, which is not limited in the embodiment of the present application.
2) Confidence determination method
For each grid in the grid map, the electronic device may determine the confidence level of the grid according to the distances between L point cloud data closest to the grid in the target lidar point cloud data and the grid, where L is an integer greater than 1, for example, l=3.
In the embodiment of the application, the electronic device may determine the confidence coefficient of the grid based on the center point of the grid, that is, the electronic device may determine the confidence coefficient of the grid according to L point cloud data closest to the center point of the grid in the target laser radar point cloud data.
Fig. 5 is a schematic structural diagram of a confidence map provided in an embodiment of the present application, as shown in fig. 5, the electronic device may determine the confidence of the grid a according to L cloud data closest to the center point O of the grid a on the X-Y plane in the target lidar point cloud data.
It should be understood that the above electronic device may also determine the confidence level of the grid based on other points of the grid, which is not limited in the embodiment of the present application
Optionally, for each grid in the grid map, the electronic device may determine an average distance between the grid and the L point cloud data according to a distance between L point cloud data closest to the grid in the target lidar point cloud data and the grid, and then determine the confidence level of the grid according to the average distance between the grid and the L point cloud data, an average distance upper threshold and an average distance lower threshold of the grid map. It should be understood that, the distances between the L point cloud data and the grid in the embodiments of the present application may refer to the distances between the L point cloud data and the center point of the grid; of course, it may also refer to distances between the above-mentioned L point cloud data and other points of the grid.
For example, the electronic device may determine the average distance between the grid and the L point cloud data according to the following formula (iii) according to the distance between the L point cloud data closest to the grid and the grid among the target lidar point cloud data.
Wherein d 0 represents the average distance between the grid and the L point cloud data, and d i represents the distance between the i-th point cloud data of the L point cloud data in the order from the near to the far.
Of course, the electronic device may determine, according to the distances between the L point cloud data closest to the grid and the grid in the target lidar point cloud data, the average distance between the grid and the L point cloud data according to other variations of the formula (iii) or an equivalent formula, which is not limited in the embodiment of the present application.
Further, the electronic device may determine the confidence level of the grid according to the following formula (fourth) according to the average distance d 0 between the grid and the L point cloud data, the average distance upper threshold value and the average distance lower threshold value of the grid map.
Where C represents the confidence level of the grid, d 0min represents the average distance lower threshold of the grid map, and d 0max represents the average distance upper threshold of the grid map.
Illustratively, d 0min may be a preset average distance lower threshold, and d 0max may be a preset average distance upper threshold. For example, d 0min may be the minimum value of d 0 corresponding to each grid in the grid map, and d 0max may be the maximum value of d 0 corresponding to each grid in the grid map.
Of course, the electronic device may determine the confidence level of the grid according to the average distance d 0 between the grid and the L point cloud data, the average distance upper threshold and the average distance lower threshold of the grid map, and other variations of the formula (four) or equivalent formulas, which is not limited in the embodiment of the present application.
Of course, the electronic device may determine the depth value and the confidence of each grid in the grid map in other manners, which is not limited in the embodiment of the present application.
In summary, in the embodiment of the application, the target laser radar point cloud data of the target soil ditch is obtained, the grid map corresponding to the target laser radar point cloud data is generated, and then the depth value and the confidence coefficient of each grid in the grid map are determined, so that the corresponding confidence coefficient map is obtained, the purpose of automatic data enhancement of the target laser radar point cloud data of the target soil ditch is achieved, a sensing device with higher configuration is not required to be arranged for the intelligent excavator, the cost of the intelligent excavator is saved, and the control device of the intelligent excavator is beneficial to accurately determining the excavating information of the next shovel of the intelligent excavator according to the confidence coefficient map.
Fig. 6 is a flowchart of a laser radar point cloud data processing method according to another embodiment of the present application. Based on the above embodiment, the embodiment of the present application introduces an implementation manner of acquiring the target lidar point cloud data of the target soil ditch in the above step S201. As shown in fig. 6, the step S201 may include:
step S201A, at least one frame of laser radar point cloud data of the target soil ditch is obtained.
The sensing device 10 in the intelligent excavator according to the embodiment of the present application rotates during the process of rotating the intelligent excavator to pour soil, etc., so that the rotation degrees corresponding to the laser radar point cloud data of different frames may be different. It should be understood that, when the sensing device 10 in the intelligent excavator collects each frame of laser radar point cloud data, the rotation degree corresponding to the frame of laser radar point cloud data may also be stored, and correspondingly, when the electronic device obtains at least one frame of laser radar point cloud data of the target soil ditch, the rotation degree corresponding to each frame of laser radar point cloud data may also be obtained simultaneously.
The rotation degree corresponding to any frame of laser radar point cloud data in the embodiment of the present application refers to the rotation degree corresponding to the time when the sensing device 10 in the intelligent excavator collects the frame of laser radar point cloud data. Illustratively, the degrees of rotation described above may include, but are not limited to, degrees of rotation sensor.
Step S201B, data preprocessing is carried out on the at least one frame of laser radar point cloud data, and the target laser radar point cloud data are obtained.
In this step, the electronic device may perform data preprocessing (e.g., fitting processing and/or registration processing) on the at least one frame of laser radar point cloud data to obtain the target laser radar point cloud data, which is favorable for the electronic device to accurately and automatically enhance the target laser radar point cloud data of the target soil ditch.
It should be understood that, if the electronic device performs data preprocessing on the single-frame lidar point cloud data, the obtained target lidar point cloud data includes the single-frame lidar point cloud data; if the electronic device performs data preprocessing on the multi-frame laser radar point cloud data, the obtained target laser radar point cloud data comprises the multi-frame laser radar point cloud data.
In a possible implementation manner, the electronic device may perform fitting processing on the at least one frame of laser radar point cloud data according to the rotation degree corresponding to each frame of the obtained laser radar point cloud data, so that three-dimensional point cloud data belonging to the same coordinate point are fitted together to obtain the target laser radar point cloud data.
Fig. 7 is a schematic diagram of multi-frame laser radar point cloud data provided by the embodiment of the application, and fig. 8 is a schematic diagram of fitting multi-frame laser radar point cloud data provided by the embodiment of the application. As shown in fig. 7, the electronic device may acquire multi-frame lidar point cloud data (such as frame 620, frame 625, frame 630, frame 635, frame 640, frame 645, frame 650, frame 655, frame 660, and frame 665 shown in fig. 7) of the target soil ditch acquired by the sensing device of the intelligent excavator, and then perform fitting processing on the multi-frame lidar point cloud data of the target soil ditch shown in fig. 7, so as to obtain lidar point cloud data (i.e., target lidar point cloud data) shown in fig. 8.
In another possible implementation manner, the electronic device may perform fitting processing on the at least one frame of lidar point cloud data according to the rotation degree corresponding to each frame of acquired lidar point cloud data, so as to obtain primary lidar point cloud data. And then, the electronic equipment can register the primary laser radar point cloud data to obtain the target laser radar point cloud data.
For example, the electronic device may perform rigid registration processing on the primary lidar point cloud data according to an iterative nearest neighbor point-to-point metric (ICP) algorithm, and of course, may perform registration processing according to other registration algorithms, which is not limited in the embodiment of the present application.
Fig. 9 is a schematic registration diagram of multi-frame laser radar point cloud data provided in an embodiment of the present application, as shown in fig. 7, the electronic device may obtain multi-frame laser radar point cloud data (such as frame 620, frame 625, frame 630, frame 635, frame 640, frame 645, frame 650, frame 655, frame 660 and frame 665 shown in fig. 7) of the target soil trench collected by the sensing device of the intelligent excavator, fit the multi-frame laser radar point cloud data of the target soil trench shown in fig. 7 to obtain laser radar point cloud data (i.e., primary laser radar point cloud data) shown in fig. 8, and then perform rigid registration processing on the primary laser radar point cloud data shown in fig. 8, so as to obtain laser radar point cloud data (i.e., target laser radar point cloud data) shown in fig. 9.
In summary, in the embodiment of the present application, at least one frame of laser radar point cloud data of the target soil ditch is obtained, and then data preprocessing is performed on the at least one frame of laser radar point cloud data to obtain the target laser radar point cloud data, which is favorable for the electronic device to accurately and automatically enhance the target laser radar point cloud data of the target soil ditch, so that the control device of the intelligent excavator can accurately determine the excavation information of the next shovel of the intelligent excavator.
Further, as can be seen from the above description, the method, the device, the equipment and the storage medium for processing the laser radar point cloud data provided by the embodiment of the application can be applied to not only multi-frame laser radar point cloud data, but also single-frame laser radar point cloud data.
Fig. 10 is a schematic diagram of data processing of single-frame laser radar point cloud data under different view angles, and as shown in fig. 10, from left to right, single-frame target laser radar point cloud data of a target soil ditch under view angles 1 and 2, a corresponding grid map of 0.1 x, and a superposition result of a confidence map and the target laser radar point cloud data are respectively shown, so that the confidence of the target laser radar point cloud data is higher in a place where the target laser radar point cloud data are denser.
Fig. 11 is a schematic diagram of comparing a grid map corresponding to single-frame laser radar point cloud data with a grid map corresponding to multiple-frame laser radar point cloud data under different viewing angles, and fig. 12 is a schematic diagram of comparing a confidence map corresponding to single-frame laser radar point cloud data with a confidence map corresponding to multiple-frame laser radar point cloud data. It should be understood that the automatic enhancement speed of single frame lidar point cloud data is faster than that of multiple frames of lidar point cloud data, but it can be seen in conjunction with fig. 11 and 12 that: the loss of the automatic enhancement details of the single-frame laser radar point cloud data is serious (such as the circle part pointed by the arrow in fig. 11 and 12), and the automatic enhancement details of the multi-frame laser radar point cloud data are reserved completely.
Fig. 13 is a schematic structural diagram of a laser radar point cloud data processing device according to an embodiment of the present application, where, as shown in fig. 13, the laser radar point cloud data processing device according to the embodiment of the present application may include: an acquisition module 1301, a generation module 1302 and a determination module 1303.
The acquiring module 1301 is configured to acquire target laser radar point cloud data of a target soil ditch;
a generating module 1302, configured to generate a grid map corresponding to the target lidar point cloud data;
The determining module 1303 is configured to determine a depth value and a confidence coefficient of each grid in the grid map, and obtain a corresponding confidence coefficient map, where the confidence coefficient map is used to determine excavation information of a next shovel of the intelligent excavator.
Optionally, the determining module 1303 includes:
The first determining unit is used for determining the depth value of each grid in the grid map according to the distances between K point cloud data closest to the grid in the target laser radar point cloud data and the grid, wherein K is an integer greater than 3.
Optionally, the first determining unit is specifically configured to:
determining normalized weights of K point cloud data according to the distances between K point cloud data closest to the grid in the point cloud data of the target laser radar and the grid;
And carrying out weighting processing on the depth values of the K point cloud data according to the normalized weights of the K point cloud data to obtain the depth values of the grid.
Optionally, the determining module 1303 includes:
the second determining unit is configured to determine, for each grid in the grid map, a confidence level of the grid according to distances between L point cloud data closest to the grid in the target lidar point cloud data and the grid, where L is an integer greater than 1.
Optionally, the second determining unit is specifically configured to:
Determining the average distance between the grid and the L point cloud data according to the distances between the L point cloud data closest to the grid in the target laser radar point cloud data and the grid;
And determining the confidence level of the grid according to the average distance between the grid and the L point cloud data, the average distance upper limit threshold value and the average distance lower limit threshold value of the grid map.
Optionally, the generating module 1302 is specifically configured to:
and generating a grid map corresponding to the target laser radar point cloud data according to the size of the target soil ditch and the preset grid cell size.
Optionally, the acquiring module 1301 includes:
The acquisition unit is used for acquiring at least one frame of laser radar point cloud data of the target soil ditch;
and the processing unit is used for carrying out data preprocessing on the at least one frame of laser radar point cloud data to obtain the target laser radar point cloud data.
Optionally, the processing unit is specifically configured to:
Fitting the at least one frame of laser radar point cloud data according to the rotation degree corresponding to each frame of the obtained laser radar point cloud data to obtain primary laser radar point cloud data;
And registering the primary laser radar point cloud data to obtain the target laser radar point cloud data.
The technical principle and the technical effect of the technical scheme about the electronic device in the embodiment of the laser radar point cloud data processing method of the present application are similar, and are not repeated here.
According to an embodiment of the present application, the present application also provides an electronic device and a readable storage medium.
According to an embodiment of the present application, there is also provided a computer program product comprising: a computer program stored in a readable storage medium, from which at least one processor of an electronic device can read, the at least one processor executing the computer program causing the electronic device to perform the solution provided by any one of the embodiments described above.
As shown in fig. 14, a block diagram of an electronic device of a laser radar point cloud data processing method according to an embodiment of the present application is shown. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device may also represent a sensing device or a control device or similar computing means in various forms of intelligent excavators. The components shown herein, their connections and relationships, and their functions, are meant to be exemplary only, and are not meant to limit implementations of the applications described and/or claimed herein.
As shown in fig. 14, the electronic device includes: one or more processors 901, memory 902, and interfaces for connecting the components, including high-speed interfaces and low-speed interfaces. The various components are interconnected using different buses and may be mounted on a common motherboard or in other manners as desired. The processor may process instructions executing within the electronic device, including instructions stored in or on memory to display graphical information of the GUI on an external input/output device, such as a display device coupled to the interface. In other embodiments, multiple processors and/or multiple buses may be used, if desired, along with multiple memories and multiple memories. Also, multiple electronic devices may be connected, each providing a portion of the necessary operations (e.g., as a server array, a set of blade servers, or a multiprocessor system). One processor 901 is illustrated in fig. 14.
Memory 902 is a non-transitory computer readable storage medium provided by the present application. The memory stores instructions executable by at least one processor to cause the at least one processor to execute the laser radar point cloud data processing method provided by the application. The non-transitory computer readable storage medium of the present application stores computer instructions for causing a computer to execute the laser radar point cloud data processing method provided by the present application.
The memory 902 is used as a non-transitory computer readable storage medium, and may be used to store non-transitory software programs, non-transitory computer executable programs, and modules, such as program instructions/modules (e.g., the acquisition module 1301, the generation module 1302, and the determination module 1303 shown in fig. 13) corresponding to the lidar point cloud data processing method in the embodiment of the present application. The processor 901 executes various functional applications of the electronic device and data processing, that is, implements the laser radar point cloud data processing method in the above-described method embodiment, by running non-transitory software programs, instructions, and modules stored in the memory 902.
The memory 902 may include a storage program area and a storage data area, wherein the storage program area may store an operating system, at least one application program required for a function; the storage data area may store data created according to the use of the electronic device described above, and the like. In addition, the memory 902 may include high-speed random access memory, and may also include non-transitory memory, such as at least one magnetic disk storage device, flash memory device, or other non-transitory solid-state storage device. In some embodiments, memory 902 optionally includes memory remotely located relative to processor 901, which may be connected to the electronic device via a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The electronic device of the laser radar point cloud data processing method of the embodiment of the application can further comprise: an input device 903 and an output device 904. The processor 901, memory 902, input devices 903, and output devices 904 may be connected by a bus or other means, for example in fig. 14.
The input device 903 may receive input numeric or character information and generate key signal inputs related to user settings and function controls of the electronic device described above, such as a touch screen, a keypad, a mouse, a track pad, a touch pad, a pointer stick, one or more mouse buttons, a track ball, a joystick, and the like. The output means 904 may include a display device, auxiliary lighting means (e.g., LEDs), tactile feedback means (e.g., vibration motors), and the like. The display device may include, but is not limited to, a Liquid Crystal Display (LCD), a Light Emitting Diode (LED) display, and a plasma display. In some implementations, the display device may be a touch screen.
Various implementations of the systems and techniques described here can be realized in digital electronic circuitry, integrated circuitry, application specific ASIC (application specific integrated circuit), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs, the one or more computer programs may be executed and/or interpreted on a programmable system including at least one programmable processor, which may be a special purpose or general-purpose programmable processor, that may receive data and instructions from, and transmit data and instructions to, a storage system, at least one input device, and at least one output device.
These computing programs (also referred to as programs, software applications, or code) include machine instructions for a programmable processor, and may be implemented in a high-level procedural and/or object-oriented programming language, and/or in assembly/machine language. As used herein, the terms "machine-readable medium" and "computer-readable medium" refer to any computer program product, apparatus, and/or device (e.g., magnetic discs, optical disks, memory, programmable Logic Devices (PLDs)) used to provide machine instructions and/or data to a programmable processor, including a machine-readable medium that receives machine instructions as a machine-readable signal. The term "machine-readable signal" refers to any signal used to provide machine instructions and/or data to a programmable processor.
To provide for interaction with a user, the systems and techniques described here can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and pointing device (e.g., a mouse or trackball) by which a user can provide input to the computer. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic input, speech input, or tactile input.
The systems and techniques described here can be implemented in a computing system that includes a background component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such background, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), wide Area Networks (WANs), and the internet.
The computer system may include a client and a server. The client and server are typically remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other.
According to the technical scheme of the embodiment of the application, the electronic equipment obtains the target laser radar point cloud data of the target soil ditch, generates the grid map corresponding to the target laser radar point cloud data, and then determines the depth value and the confidence coefficient of each grid in the grid map to obtain the corresponding confidence coefficient map, so that the aim of automatically enhancing the target laser radar point cloud data of the target soil ditch is fulfilled, a sensing device with higher configuration is not required to be arranged for the intelligent excavator, the cost of the intelligent excavator can be saved, and the control equipment of the intelligent excavator can accurately determine the excavating information of the next shovel of the intelligent excavator according to the confidence coefficient map.
It should be appreciated that various forms of the flows shown above may be used to reorder, add, or delete steps. For example, the steps described in the present application may be performed in parallel, sequentially, or in a different order, provided that the desired results of the disclosed embodiments are achieved, and are not limited herein.
The above embodiments do not limit the scope of the present application. It will be apparent to those skilled in the art that various modifications, combinations, sub-combinations and alternatives are possible, depending on design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present application should be included in the scope of the present application.

Claims (15)

1. The laser radar point cloud data processing method is characterized by comprising the following steps of:
Acquiring target laser radar point cloud data of a target soil ditch;
Generating a grid map corresponding to the target laser radar point cloud data;
determining the depth value and the confidence coefficient of each grid in the grid map to obtain a corresponding confidence coefficient map, wherein the confidence coefficient map is used for determining the digging information of the next shovel of the intelligent excavator;
the determining the depth value of each grid in the grid map comprises the following steps:
Determining normalized weights of K point cloud data according to the distances between K point cloud data closest to the grid and the grid in the target laser radar point cloud data aiming at each grid in the grid map; and carrying out weighting processing on the depth values of the K point cloud data according to the normalized weights of the K point cloud data to obtain the depth values of the grid, wherein K is an integer greater than 3.
2. The method of claim 1, wherein determining the confidence level for each grid in the grid map comprises:
And determining the confidence level of each grid in the grid map according to the distances between the L nearest point cloud data to the grid in the target laser radar point cloud data and the grid, wherein L is an integer greater than 1.
3. The method according to claim 2, wherein determining the confidence level of the grid according to the distances between the closest L point cloud data to the grid among the target lidar point cloud data and the grid includes:
Determining the average distance between the grid and the L point cloud data according to the distances between the L point cloud data closest to the grid in the target laser radar point cloud data and the grid;
And determining the confidence level of the grid according to the average distance between the grid and the L point cloud data, the average distance upper limit threshold value and the average distance lower limit threshold value of the grid map.
4. The method of claim 1, wherein generating the grid map corresponding to the target lidar point cloud data comprises:
and generating a grid map corresponding to the target laser radar point cloud data according to the size of the target soil ditch and the preset grid cell size.
5. The method of claim 1, wherein the acquiring target lidar point cloud data for the target trench comprises:
acquiring at least one frame of laser radar point cloud data of a target soil ditch;
and carrying out data preprocessing on the at least one frame of laser radar point cloud data to obtain the target laser radar point cloud data.
6. The method of claim 5, wherein the performing data preprocessing on the at least one frame of lidar point cloud data to obtain the target lidar point cloud data comprises:
Fitting the at least one frame of laser radar point cloud data according to the rotation degree corresponding to each frame of the obtained laser radar point cloud data to obtain primary laser radar point cloud data;
And registering the primary laser radar point cloud data to obtain the target laser radar point cloud data.
7. A lidar point cloud data processing device, comprising:
the acquisition module is used for acquiring target laser radar point cloud data of the target soil ditch;
The generation module is used for generating a grid map corresponding to the target laser radar point cloud data;
the determining module is used for determining the depth value and the confidence coefficient of each grid in the grid map to obtain a corresponding confidence coefficient map, wherein the confidence coefficient map is used for determining the digging information of the next shovel of the intelligent excavator;
The determining module includes:
The first determining unit is used for determining the normalized weights of K point cloud data according to the distances between K point cloud data closest to the grid and the grid in the target laser radar point cloud data aiming at each grid in the grid map; and carrying out weighting processing on the depth values of the K point cloud data according to the normalized weights of the K point cloud data to obtain the depth values of the grid, wherein K is an integer greater than 3.
8. The apparatus of claim 7, wherein the determining module comprises:
the second determining unit is configured to determine, for each grid in the grid map, a confidence level of the grid according to distances between L point cloud data closest to the grid in the target lidar point cloud data and the grid, where L is an integer greater than 1.
9. The apparatus according to claim 8, wherein the second determining unit is specifically configured to:
Determining the average distance between the grid and the L point cloud data according to the distances between the L point cloud data closest to the grid in the target laser radar point cloud data and the grid;
And determining the confidence level of the grid according to the average distance between the grid and the L point cloud data, the average distance upper limit threshold value and the average distance lower limit threshold value of the grid map.
10. The apparatus of claim 7, wherein the generating module is specifically configured to:
and generating a grid map corresponding to the target laser radar point cloud data according to the size of the target soil ditch and the preset grid cell size.
11. The apparatus of claim 7, wherein the acquisition module comprises:
The acquisition unit is used for acquiring at least one frame of laser radar point cloud data of the target soil ditch;
and the processing unit is used for carrying out data preprocessing on the at least one frame of laser radar point cloud data to obtain the target laser radar point cloud data.
12. The apparatus according to claim 11, wherein the processing unit is specifically configured to:
Fitting the at least one frame of laser radar point cloud data according to the rotation degree corresponding to each frame of the obtained laser radar point cloud data to obtain primary laser radar point cloud data;
And registering the primary laser radar point cloud data to obtain the target laser radar point cloud data.
13. An electronic device, comprising:
At least one processor; and a memory communicatively coupled to the at least one processor;
wherein the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-6.
14. A non-transitory computer readable storage medium storing computer instructions for causing the computer to perform the method of any one of claims 1-6.
15. A computer program product comprising a computer program which, when executed by a processor, implements the method according to any of claims 1-6.
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