CN115629368A - Method and system for generating simulated dynamic point cloud data - Google Patents
Method and system for generating simulated dynamic point cloud data Download PDFInfo
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- CN115629368A CN115629368A CN202211413938.7A CN202211413938A CN115629368A CN 115629368 A CN115629368 A CN 115629368A CN 202211413938 A CN202211413938 A CN 202211413938A CN 115629368 A CN115629368 A CN 115629368A
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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
- 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
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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
- 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
- G01S7/4802—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S17/00 using analysis of echo signal for target characterisation; Target signature; Target cross-section
Abstract
The application relates to a method and a system for generating simulated dynamic environment point cloud data, belonging to the technical field of point cloud data processing, wherein the method comprises the following steps: acquiring point cloud data with obstacles scanned by a multi-line laser radar; dividing the laser radar point cloud data into a plurality of cubic lattices with the same size by using voxel three-dimensional gridding, wherein each lattice can be simulated as an obstacle in a three-dimensional space; obtaining random quantity and random position of barrier grids, and simulating a real environment to obtain the quantity and position randomness of the barriers; adding Gaussian noise to a laser radar point cloud grid at a position to simulate obstacles in a real environment; and outputting the processed laser radar point cloud data. The invention can obtain the simulated dynamic environment point cloud data without recording the real dynamic environment point cloud data for many times.
Description
Technical Field
The invention relates to the technical field of point cloud data processing, in particular to a method and a system for generating simulated dynamic point cloud data.
Background
The simulated dynamic environment point cloud data is laser radar point cloud data based on an original dynamic environment, a limited point cloud data set can only represent partial conditions of real data distribution, and unless the data set is infinite (continuous distribution) or contains all possible data (discrete distribution), all real data distribution of the data cannot be completely tested through experiments.
Generally, there are two ways for recording point cloud data in dynamic environment. In the first method, a simulation tool is used in a simulation environment to simulate the state of an obstacle and simulate the working principle of a sensor. The method comprises the steps of artificially making a three-dimensional dynamic environment, performing scene simulation, and recording point cloud data of the three-dimensional dynamic environment by using a virtual sensor such as a camera or a laser radar. The second mode is that a large amount of manpower and material resources are utilized to select a road, static and dynamic obstacles are placed on the road, and laser radar is used for acquiring high-precision and high-density three-dimensional dynamic environment point cloud data in a real environment.
In the first method, the simulation model is expensive to manufacture. Under the existing three-dimensional modeling technical conditions, a scene with strong reality sense or a model of a single obstacle cannot be made fully automatically, and a fully-manual or semi-automatic modeling mode is required. Therefore, the manufacturing of large-scale scenes and models of a large number of obstacles requires high investment in manpower and material resources. And the simulation environment has a large error with the real environment, and lacks practical application significance. The second method, the real dynamic environment is difficult to control, is subject to external influences and is expensive. And the data volume is very large, and the laser radar is inconvenient to transport, so that the cost for generating the three-dimensional scene map is increased. Therefore, the dynamic environment laser radar data recording cost is high and difficulty is high.
Disclosure of Invention
The invention aims to provide a method for generating simulated dynamic point cloud data, which can obtain simulated dynamic environment point cloud data without recording real dynamic environment point cloud data for many times.
The invention provides a method for generating simulated dynamic environment point cloud data, which comprises the following steps:
acquiring point cloud data with obstacles scanned by a multi-line laser radar;
dividing point cloud data into a plurality of cubic lattices with the same size by using voxel three-dimensional grid division, wherein each lattice can be simulated as an obstacle in a three-dimensional space;
setting a cubic lattice with random number and random positions, and simulating the number and position randomness of the obstacles acquired in the real environment.
Preferably, the obtaining a random number and a random position of the obstacle grid simulates the randomness of the number and the position of the real environment obtaining obstacles, and then further comprises:
adding Gaussian noise to a point cloud grid at a position to simulate an obstacle in a real environment;
and outputting the processed point cloud data.
Preferably, the dividing the point cloud data into a plurality of cubic grids with the same size by using voxel three-dimensional meshing on the point cloud data, wherein each grid can be simulated as an obstacle in a three-dimensional space comprises:
inputting point cloud data of a plurality of points,
acquiring a single frame of pcd point cloud, and using a voxel grid for point cloud data;
voxel grid sets the voxel size of the whole point cloud according to the LeafSize, namely, the whole point cloud space is divided into a plurality of small cubes with the LeafSize as the basic unit, and each grid is used as the candidate grid of the obstacle.
Preferably, the setting of the cubic lattice with random number and random position specifically includes, by simulating the number and position randomness of the real environment acquisition obstacles:
acquiring a random number and a random position of the dynamic object grids,
acquiring the number of set lattices, namely, leafSize _ len, and traversing 1 to the lattices of the LeafSize _ len;
and using a rand () function, assigning each lattice a random-size number between 0 and 1, setting the probability of obtaining the lattice to be a number between 0 and 1, setting the number assigned to each lattice to be r, obtaining the probability of the lattice to be n, and obtaining the lattice only when r is more than n.
Preferably, the step of adding gaussian noise to the lidar point cloud grid at one position includes:
adding Gaussian noise into the obtained random grid, wherein the probability density of the Gaussian noise follows Gaussian distribution, and the Gaussian distribution has two parameters of mean and standard deviation sigma, and the formula is as follows:
the values of μ and σ are the average of the corresponding grids, and voxel _ size/4.0, and the number of noise points to be added per grid is also determined by calculation.
The second purpose of the invention can be achieved by adopting the following technical scheme: a system for generating simulated dynamic environment point cloud data, comprising:
the data acquisition module is used for acquiring point cloud data which are scanned by the multi-line laser radar and have obstacles;
the data processing module is used for dividing the point cloud data into a plurality of cubic lattices with the same size by using a voxel three-dimensional grid, and each lattice can be simulated as an obstacle in a three-dimensional space;
and the cube lattice dividing module is used for setting a random number and a random position of cube lattices and simulating the number and the position randomness of the obstacles acquired in the real environment.
Preferably, the cube lattice dividing module is configured to set a random number and a random position of cube lattices, and simulate the number and the position randomness of the obstacle acquired in the real environment, and includes:
a data input module for inputting point cloud data,
the point cloud data processing module is used for acquiring a single frame of pcd point cloud and using a voxel grid for point cloud data;
and the voxel grid processing module is used for setting the voxel size of the whole point cloud according to LeafSize by a voxel filter pcl, namely dividing the whole point cloud space into a plurality of small cubes taking the LeafSize as a basic unit, and enabling each grid to be used as the candidate grid of the obstacle.
The third purpose of the invention can be achieved by adopting the following technical scheme:
a computer device comprises a processor and a memory for storing a program executable by the processor, and when the processor executes the program stored by the memory, the method for generating the simulated dynamic point cloud data is realized.
The fourth purpose of the invention can be achieved by adopting the following technical scheme:
a storage medium stores a program which, when executed by a processor, implements the above-described method for generating simulated dynamic point cloud data.
The method comprises the steps of obtaining a section of point cloud data which is scanned by a laser radar and has obstacles, using the point cloud data as original point cloud data, carrying out voxel three-dimensional grid division on the original laser radar point cloud data, dividing the laser radar point cloud data into a plurality of cubes with the same size, selecting a random number of cube grids in the laser radar point cloud data divided into three-dimensional grids to simulate the randomness of the number of obstacles in a real environment, selecting random positions from the selected number of point cloud grids to simulate the randomness of the positions of the obstacles in the real environment, adding Gaussian noise in the laser radar point cloud grids in the selected positions to simulate the obstacles in the real environment, and outputting the processed laser radar point cloud data. The diversity of the dynamic environment point cloud data of the laser radar and the controllability of the dynamic environment point cloud data of the laser radar are increased, and the simulated dynamic environment point cloud data can be obtained without recording the real dynamic environment point cloud data for many times.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and together with the description, serve to explain the principles of the invention.
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious for those skilled in the art that other drawings can be obtained according to the drawings without inventive exercise.
FIG. 1 is a flow chart of a method for generating simulated dynamic environment point cloud data according to the present invention;
fig. 2 is another flowchart of a method for generating simulated dynamic environment point cloud data according to the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
It should be noted that all directional indicators (such as up, down, left, right, front, and back \8230;) in the embodiments of the present invention are only used to explain the relative positional relationship between the components, the motion situation, etc. in a specific posture (as shown in the attached drawings), and if the specific posture is changed, the directional indicators are changed accordingly.
In addition, the descriptions relating to "first", "second", etc. in the present invention are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one such feature. In addition, technical solutions between various embodiments may be combined with each other, but must be realized by a person skilled in the art, and when the technical solutions are contradictory or cannot be realized, such a combination should not be considered to exist, and is not within the protection scope of the present invention.
The invention provides a method for generating simulated dynamic environment point cloud data, which refers to the images in figures 1-2 and comprises the following steps:
step 100, acquiring point cloud data with obstacles scanned by a multi-line laser radar;
step 200, point cloud data are divided into a plurality of cubic grids with the same size by using voxel three-dimensional grid division, and each grid can be simulated as an obstacle in a three-dimensional space;
and 300, setting a random number and a random position of cubic lattices, and simulating the number and the position randomness of the obstacles acquired in the real environment.
The method comprises the steps of obtaining a section of point cloud data which is scanned by a laser radar and has obstacles, using the point cloud data as original point cloud data, carrying out voxel three-dimensional grid division on the original laser radar point cloud data, dividing the laser radar point cloud data into a plurality of cubes with the same size, selecting a random number of cube grids in the laser radar point cloud data divided into three-dimensional grids to simulate the randomness of the number of obstacles in a real environment, selecting random positions from the selected number of point cloud grids to simulate the randomness of the positions of the obstacles in the real environment, adding Gaussian noise in the laser radar point cloud grids in the selected positions to simulate the obstacles in the real environment, and outputting the processed laser radar point cloud data. The diversity of the dynamic environment point cloud data of the laser radar and the controllability of the dynamic environment point cloud data of the laser radar are increased, and the simulated dynamic environment point cloud data can be obtained without recording the real dynamic environment point cloud data for many times.
Preferably, step 300, obtaining a random number and a random position of the obstacle grid, simulating a real environment to obtain the number and the position randomness of the obstacles, and then further comprising:
step 400, gaussian noise is added into a point cloud grid at a position to simulate obstacles in a real environment;
and 500, outputting the processed point cloud data.
Preferably, the step 200 of dividing the point cloud data into a plurality of cubic grids with the same size by using voxel three-dimensional meshing, wherein each grid can be simulated as an obstacle in a three-dimensional space includes:
step 210, inputting the point cloud data,
step 220, acquiring a single frame of pcd point cloud, and using a voxel grid for point cloud data;
and step 230, setting the voxel size of the whole point cloud according to the LeafSize by a voxel filter pcl, namely, dividing the whole point cloud space into a plurality of small cubes taking the LeafSize as a basic unit, and enabling each grid to be used as the candidate grid of the obstacle.
Preferably, step 300, setting a random number and a random position of a cubic grid, and simulating the number and the position randomness of the real environment acquisition obstacles specifically includes:
step 310, acquiring a random number and a random position of the dynamic object grid,
step 320, acquiring the number of the set lattices LeafSize _ len, and traversing 1 to the lattices of the LeafSize _ len;
in a step 330 of the method,
and using a rand () function, assigning a random-size number between 0 and 1 to each grid, setting the probability of obtaining the grid to be a number between 0 and 1, setting the number assigned to each grid to be r, obtaining the probability of the grid to be n, and obtaining the grid only when r is more than n.
Preferably, in step 400, gaussian noise is added to the lidar point cloud grid at one position, and simulating an obstacle in a real environment specifically includes:
adding Gaussian noise into the obtained random grid, wherein the probability density of the Gaussian noise follows Gaussian distribution, and the Gaussian distribution has two parameters of mean and standard deviation sigma, and the formula is as follows:
the values of μ and σ are the average of the corresponding grids, and voxel _ size/4.0, and the number of noise points to be added per grid is also determined by calculation.
The second purpose of the invention can be achieved by adopting the following technical scheme: a system for generating simulated dynamic environment point cloud data, comprising:
the data acquisition module is used for acquiring point cloud data with obstacles scanned by the multi-line laser radar;
the data processing module is used for dividing the point cloud data into a plurality of cubic lattices with the same size by using a voxel three-dimensional grid, and each lattice can be simulated as an obstacle in a three-dimensional space;
and the cube lattice dividing module is used for setting a random number and a random position of cube lattices and simulating the number and the position randomness of the obstacles acquired in the real environment.
The method comprises the steps of obtaining a section of point cloud data which is scanned by a laser radar and has obstacles, using the point cloud data as original point cloud data, carrying out voxel three-dimensional grid division on the original laser radar point cloud data, dividing the laser radar point cloud data into a plurality of cubes with the same size, selecting a random number of cube grids in the laser radar point cloud data divided into three-dimensional grids to simulate the randomness of the number of obstacles in a real environment, selecting random positions from the selected number of point cloud grids to simulate the randomness of the positions of the obstacles in the real environment, adding Gaussian noise in the laser radar point cloud grids in the selected positions to simulate the obstacles in the real environment, and outputting the processed laser radar point cloud data. The diversity of the dynamic environment point cloud data of the laser radar and the controllability of the dynamic environment point cloud data of the laser radar are increased, and the simulated dynamic environment point cloud data can be obtained without recording the real dynamic environment point cloud data for many times.
Preferably, the cube lattice dividing module is configured to set a random number and a random position of cube lattices, and simulate the number and the position randomness of the obstacle acquired in the real environment, and includes:
a data input module for inputting point cloud data,
the point cloud data processing module is used for acquiring a single frame of pcd point cloud and using a voxel grid for point cloud data;
and the voxel grid processing module is used for setting the voxel size of the whole point cloud according to LeafSize in the voxel filter pcl, namely, dividing the whole point cloud space into a plurality of small cubes taking LeafSize as a basic unit, and enabling each grid to be used as the candidate grid of the obstacle.
Example 3:
the present embodiment provides a computer device, which includes a processor and a memory for storing a processor executable program, and when the processor executes the program stored in the memory, the method for generating simulated dynamic point cloud data according to the foregoing embodiment 1 is implemented, including: acquiring point cloud data with obstacles scanned by a multi-line laser radar; dividing point cloud data into a plurality of cubic lattices with the same size by using voxel three-dimensional grid division, wherein each lattice can be simulated as an obstacle in a three-dimensional space; setting a cubic lattice with random number and random positions, and simulating the number and position randomness of the obstacles acquired in the real environment.
Example 4:
the present embodiment provides a storage medium, which is a computer-readable storage medium, and stores a computer program, where when the program is executed by a processor, and the processor executes the computer program stored in a memory, the method for generating simulated dynamic point cloud data according to embodiment 1 above is implemented, where the method includes: acquiring point cloud data with obstacles scanned by a multi-line laser radar; dividing point cloud data into a plurality of cubic lattices with the same size by using voxel three-dimensional grid division, wherein each lattice can be simulated as an obstacle in a three-dimensional space; setting a cubic lattice with random number and random positions, and simulating the number and position randomness of the obstacles acquired in the real environment.
The foregoing are merely exemplary embodiments of the present invention, which enable those skilled in the art to understand or practice the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (9)
1. A method for generating simulated dynamic environment point cloud data is characterized by comprising the following steps:
acquiring point cloud data with obstacles scanned by a multi-line laser radar;
dividing point cloud data into a plurality of cubic lattices with the same size by using voxel three-dimensional grid division, wherein each lattice can be simulated as an obstacle in a three-dimensional space;
setting a cubic lattice with random number and random positions, and simulating the number and position randomness of the obstacles acquired in the real environment.
2. The method as claimed in claim 1, wherein the grid of obstacles with random number and random positions is obtained by simulating the randomness of the number and positions of obstacles obtained in real environment, and then further comprising:
adding Gaussian noise to a point cloud grid at a position to simulate an obstacle in a real environment;
and outputting the processed point cloud data.
3. The method of claim 1, wherein the step of using voxel three-dimensional meshing to divide the point cloud data into a plurality of cubic grids of the same size, each grid being capable of being modeled as an obstacle in three-dimensional space comprises:
inputting the point cloud data of the point cloud,
acquiring a single frame of pcd point cloud, and using a voxel grid for point cloud data;
voxel grid sets the voxel size of the whole point cloud according to the LeafSize, namely, the whole point cloud space is divided into a plurality of small cubes with the LeafSize as the basic unit, and each grid is used as the candidate grid of the obstacle.
4. The method as claimed in claim 1, wherein the setting of the random number and random position cubic lattice specifically includes:
acquiring a random number and a random position of the dynamic object grids,
acquiring the number of set lattices, namely, leafSize _ len, and traversing 1 to the lattices of the LeafSize _ len;
and using a rand () function, assigning each lattice a random-size number between 0 and 1, setting the probability of obtaining the lattice to be a number between 0 and 1, setting the number assigned to each lattice to be r, obtaining the probability of the lattice to be n, and obtaining the lattice only when r is more than n.
5. The method as claimed in claim 1, wherein the step of adding gaussian noise to the lidar point cloud grid at one location includes:
adding Gaussian noise into the obtained random grid, wherein the probability density of the Gaussian noise follows Gaussian distribution, and the Gaussian distribution has two parameters of mean and standard deviation sigma, and the formula is as follows:
the values of μ and σ are the average of the corresponding grids, and voxel _ size/4.0, and the number of noise points to be added per grid is also determined by calculation.
6. A system for generating simulated dynamic environment point cloud data, comprising:
the data acquisition module is used for acquiring point cloud data with obstacles scanned by the multi-line laser radar;
the data processing module is used for dividing the point cloud data into a plurality of cubic lattices with the same size by using a voxel three-dimensional grid, and each lattice can be simulated as an obstacle in a three-dimensional space;
and the cube grid dividing module is used for setting cube grids with random quantity and random positions and simulating the quantity and the position randomness of the obstacles acquired in the real environment.
7. The system of claim 6, wherein the system further comprises a data storage device for storing the simulated dynamic environment point cloud data,
the cube lattice dividing module is used for setting a random number and a random position of cube lattices, and simulating the number and the position randomness of the obstacles acquired in the real environment, and comprises the following steps:
a data input module for inputting point cloud data,
the point cloud data processing module is used for acquiring a single frame of pcd point cloud and using a voxel grid for point cloud data;
and the voxel grid processing module is used for setting the voxel size of the whole point cloud according to LeafSize in the voxel filter pcl, namely, dividing the whole point cloud space into a plurality of small cubes taking LeafSize as a basic unit, and enabling each grid to be used as the candidate grid of the obstacle.
8. A computer device comprising a processor and a memory for storing processor-executable programs, the computer device performing the method of any of claims 1 to 5 when the processor executes a program stored in the memory.
9. A storage medium, characterized by storing a program which, when executed by a processor, performs the method of any one of claims 1 to 5.
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