CN109145677A - Obstacle detection method, device, equipment and storage medium - Google Patents
Obstacle detection method, device, equipment and storage medium Download PDFInfo
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- CN109145677A CN109145677A CN201710452994.4A CN201710452994A CN109145677A CN 109145677 A CN109145677 A CN 109145677A CN 201710452994 A CN201710452994 A CN 201710452994A CN 109145677 A CN109145677 A CN 109145677A
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V20/00—Scenes; Scene-specific elements
- G06V20/50—Context or environment of the image
- G06V20/56—Context or environment of the image exterior to a vehicle by using sensors mounted on the vehicle
- G06V20/58—Recognition of moving objects or obstacles, e.g. vehicles or pedestrians; Recognition of traffic objects, e.g. traffic signs, traffic lights or roads
Abstract
The invention discloses obstacle detection method, device, equipment and storage mediums, and wherein method includes: to be scanned to obtain three-dimensional point cloud to automatic driving vehicle ambient enviroment；The three-dimensional point cloud is subjected to down-sampling, is projected on grid, grid image is constructed；Detect the barrier in the grid image.All barriers all can be completed to divide, barrier will not be missed；It can satisfy the demand of real-time.
Description
[technical field]
The present invention relates to Computer Applied Technologies, in particular to obstacle detection method, device, equipment and storage medium.
[background technique]
Automatic driving vehicle, alternatively referred to as automatic driving vehicle refer to through various sensors and perceive vehicle periphery ring
Border, and according to road, vehicle location and obstacle information obtained etc. is perceived, the steering and speed of vehicle are controlled, to make
Vehicle can be travelled reliably and securely on road.
The 3d disturbance of perception quality testing survey technology that automatic driving vehicle uses, with laser radar (LiDAR, Light Laser
Detection and Ranging) vehicle-surroundings surrounding three-dimensional range information is obtained, laser radar scanning one encloses scene, returns
The point cloud data of scene three-dimensional space, i.e., three-dimensional (3D) point cloud data.Environment sensing obstacle based on vehicle-mounted three-dimensional laser radar
Quality testing survey technology can directly acquire object dimensional apart from shape information, have measurement accuracy height, change to light environment unwise
The advantages of sense.
Based on the three dimensional point cloud scanned, running environment is perceived by distance analysis identification technology, it can be into
Detection and the identification of obstacle identity of row barrier etc. export obstacle information.Including shadow possible on vehicle, pedestrian, ground
Ring trafficability, various other movements of safety or the identification of stationary object.So that automatic driving vehicle carries out avoidance behaviour
Make etc..
In order to enable the vehicle to reliably and securely travel on road, need in the process of moving, can in real time, it is accurate
Identify that there may be the objects of security risk to driving safety on driving path periphery, take necessary operation to keep away for vehicle itself
Exempt from that traffic accidents occur.
In the prior art, a common detection of obstacles algorithm includes the step such as ground point removal, object detection, identification
Suddenly.Wherein the quality of object detection directly affects subsequent identification and tracking.
Object detection is carried out using cutting techniques, difficult point is how to reduce over-segmentation and less divided.It is also desirable to energy
Enough meets the needs of real-time.
Since laser radar cost is higher etc., factors, existing environment perception technology scheme are based on based on 2D vision
The research of the 3d cognition technology of laser radar is simultaneously insufficient.Object detection algorithm based on sliding window is in current public data
The 3D detection of obstacles algorithm that behaves oneself best on collection KITTI, its working principle are as follows: firstly, turning to 3D net for cloud is discrete
Lattice.Secondly, point characteristic value corresponding with its in grid to be mapped to the spy of a fixed dimension to each non-empty grid
It levies in vector.For empty grid, map that in 0 vector.In this way, a cloud is just converted to a feature net
Lattice.Then, it is slided in three dimensions with the sliding window of a 3D, it is one that a sliding window is assessed with a classifier
A possibility that a barrier.The above-mentioned algorithm of special angle repeated attempt finally is rotated to each sliding window.
But sliding window algorithm has the disadvantage in that 1. robustness are poor.Need the object to variety classes all size
Body distinguishes custom window；Window can only attempt limited discrete value on direction, cannot cover all directions.2. can only detect
A small number of known type barriers need to carry out 3. single frames of secondary splitting using additional algorithm to examine in order to avoid barrier missing inspection
The time is surveyed in 400ms or more, is not able to satisfy the demand of real-time.
[summary of the invention]
The many aspects of the application provide obstacle detection method, device, equipment and storage medium, can be to all
Barrier is all completed to divide, and will not miss barrier；Improve the real-time of detection of obstacles.
The one side of the application provides a kind of obstacle detection method, comprising:
Automatic driving vehicle ambient enviroment is scanned to obtain three-dimensional point cloud；
The three-dimensional point cloud is subjected to down-sampling, is projected on grid, grid image is constructed；
Detect the barrier in the grid image.
The aspect and any possible implementation manners as described above, it is further provided a kind of implementation, it is described will be described
Three-dimensional point cloud projects on grid
The three-dimensional point cloud is subjected to down-sampling, is projected on the two-dimensional grid parallel with ground；Or,
The three-dimensional point cloud is subjected to down-sampling, is projected on 3D grid；Or,
The three-dimensional point cloud is subjected to down-sampling, before projecting on the two-dimensional grid at visual angle.
The aspect and any possible implementation manners as described above, it is further provided a kind of implementation, the detection institute
The barrier stated in grid image further comprises:
Detection of obstacles is carried out to the grid image using region growing algorithm.
The aspect and any possible implementation manners as described above, it is further provided a kind of implementation, it is described will be described
Three-dimensional point cloud carries out down-sampling, projects on the two-dimensional grid parallel with ground and further includes:
The peak in mean value and the direction z in each grid record cylindricality on all the points x, y direction.
The aspect and any possible implementation manners as described above, it is further provided a kind of implementation, it is described will be described
Three-dimensional point cloud carries out down-sampling, projects on 3D grid and further includes:
Each grid records the mean value of all the points in the grid.
The aspect and any possible implementation manners as described above, it is further provided a kind of implementation, by the three-dimensional
Point cloud carries out down-sampling, further includes: on the two-dimensional grid at visual angle before projecting
Each grid records in the grid laser scanning point to the depth information of laser emitter.
The aspect and any possible implementation manners as described above, it is further provided a kind of implementation, it is described to use area
Grid image described in the growth algorithm of domain carries out detection of obstacles, including following sub-step:
Sub-step 1, randomly selected from grid image 1 not yet ownership pixel as seed point, if the pixel is
(x0, y0), with stack representation seed set, by seed point (x0, y0) push into seed storehouse；
First seed point (x0, y0) pop in seed storehouse is gone out storehouse by sub-step 2, and centered on the point, traversal
The center neighborhood territory pixel (x, y)；
Whether judgement traversal pixel (x, y) is in seed region；If not, whether judgement traversal pixel (x, y) is full
Smoothness constraint condition between foot and seed point (x0, y0)；If traversal pixel (x, y) meets condition, by the traversal pixel
(x, y) push is into storehouse；Meanwhile cluster set is added in seed point (x0, y0)；
Sub-step 3 judges whether seed storehouse is empty；If so, by cluster gather in pixel constitute one
Cluster, the cluster are the corresponding pixel collection of barrier, execute sub-step 4；If not, executing sub-step 2；
Sub-step 4 judges whether all pixels were all once added into seed storehouse in 2-D gray image, if so,
Region increases and terminates, and exports all cluster；If not, executing sub-step 1.
The aspect and any possible implementation manners as described above, it is further provided a kind of implementation, it is described to use area
Grid image described in the growth algorithm of domain carries out detection of obstacles, further comprises: according to the three-dimensional point stored during down-sampling
Corresponding relationship between cloud and the pixel of grid obtains the corresponding three-dimensional point cloud of barrier.
Another aspect of the present invention provides a kind of obstacle detector, comprising: acquiring unit, projecting unit and inspection
Survey unit；
The acquiring unit, for being scanned to obtain three-dimensional point cloud to automatic driving vehicle ambient enviroment；
The map unit projects on grid for the three-dimensional point cloud to be carried out down-sampling, constructs grid image；
The taxon, for detecting the barrier in the grid image.
The aspect and any possible implementation manners as described above, it is further provided a kind of implementation, the mapping are single
Member is specifically used for:
The three-dimensional point cloud is subjected to down-sampling, is projected on the two-dimensional grid parallel with ground；Or,
The three-dimensional point cloud is subjected to down-sampling, is projected on 3D grid；Or,
The three-dimensional point cloud is subjected to down-sampling, before projecting on the two-dimensional grid at visual angle.
The aspect and any possible implementation manners as described above, it is further provided a kind of implementation, the grouping sheet
Member is specifically used for:
Detection of obstacles is carried out to the grid image using region growing algorithm.
The aspect and any possible implementation manners as described above, it is further provided a kind of implementation, the mapping are single
The three-dimensional point cloud is being carried out down-sampling by member, specific to execute when projecting on the two-dimensional grid parallel with ground:
The peak in mean value and the direction z in cylindricality on all the points x, y direction is recorded in each grid.
The aspect and any possible implementation manners as described above, it is further provided a kind of implementation, the mapping are single
The three-dimensional point cloud is being carried out down-sampling by member, specific to execute when projecting on 3D grid:
Each grid records the mean value of all the points in the grid.
The aspect and any possible implementation manners as described above, it is further provided a kind of implementation, the mapping are single
The three-dimensional point cloud is being carried out down-sampling by member, specific to execute when before projecting on the two-dimensional grid at visual angle:
Each grid records in the grid laser scanning point to the depth information of laser emitter.
The aspect and any possible implementation manners as described above, it is further provided a kind of implementation, the grouping sheet
Member is specific to execute when using area growth algorithm carries out detection of obstacles to the grid image:
Sub-step 1, randomly selected from grid image 1 not yet ownership pixel as seed point, if the pixel is
(x0, y0), with stack representation seed set, by seed point (x0, y0) push into seed storehouse；
First seed point (x0, y0) pop in seed storehouse is gone out storehouse by sub-step 2, and centered on the point, traversal
Center neighborhood territory pixel (x, y)；
Whether judgement traversal pixel (x, y) is in seed region；If not, whether judgement traversal pixel (x, y) is full
Smoothness constraint condition between foot and seed point (x0, y0)；If traversal pixel (x, y) meets condition, by the traversal pixel
(x, y) push is into storehouse；Meanwhile cluster set is added in seed point (x0, y0)；
Sub-step 3 judges whether seed storehouse is empty；If so, by cluster gather in pixel constitute one
Cluster, the cluster are the corresponding pixel collection of barrier, execute sub-step 4；If not, executing sub-step 2；
Sub-step 4 judges whether all pixels were all once added into seed storehouse in 2-D gray image, if so,
Region increases and terminates, and exports all cluster,；If not, executing sub-step 1.
The aspect and any possible implementation manners as described above, it is further provided a kind of implementation, the grouping sheet
Member is specific to execute when using area growth algorithm carries out detection of obstacles to the grid image:
According to the corresponding relationship between the pixel of the three-dimensional point cloud and grid that store during down-sampling, barrier pair is obtained
The three-dimensional point cloud answered.
Another aspect of the present invention, provides a kind of computer equipment, including memory, processor and is stored in the storage
On device and the computer program that can run on the processor, the processor are realized as previously discussed when executing described program
Method.
Another aspect of the present invention provides a kind of computer readable storage medium, is stored thereon with computer program, described
Method as described above is realized when program is executed by processor.
It can be seen that based on above-mentioned introduction using scheme of the present invention, three-dimensional point cloud projected on grid, construct net
Table images detect the barrier in the grid image using region growing algorithm.All barriers are all completed to divide, no
Barrier can be missed, can satisfy the demand of real-time.
[Detailed description of the invention]
Fig. 1 is the flow chart of obstacle detection method embodiment of the present invention；
Fig. 2 is the schematic diagram for the laser point cloud that mobile lidar acquires barrier object in the embodiment of the present invention；
Fig. 3 is the flow chart of another embodiment of obstacle detection method of the present invention；
Fig. 4 is the flow chart of another embodiment of obstacle detection method of the present invention；
Fig. 5 is the composed structure schematic diagram of obstacle detector embodiment of the present invention；
Fig. 6 shows the frame for being suitable for the exemplary computer system/server 012 for being used to realize embodiment of the present invention
Figure.
[specific embodiment]
To keep the purposes, technical schemes and advantages of the embodiment of the present application clearer, below in conjunction with the embodiment of the present application
In attached drawing, the technical scheme in the embodiment of the application is clearly and completely described, it is clear that described embodiment is
Some embodiments of the present application, instead of all the embodiments.Based on the embodiment in the application, those of ordinary skill in the art
Whole other embodiments obtained without creative efforts, shall fall in the protection scope of this application.
In addition, the terms "and/or", only a kind of incidence relation for describing affiliated partner, indicates may exist
Three kinds of relationships, for example, A and/or B, can indicate: individualism A exists simultaneously A and B, these three situations of individualism B.Separately
Outside, character "/" herein typicallys represent the relationship that forward-backward correlation object is a kind of "or".
Fig. 1 is the flow chart of obstacle detection method embodiment of the present invention, as shown in Figure 1, comprising the following steps:
In 101, automatic driving vehicle ambient enviroment is scanned to obtain three-dimensional point cloud.
It in the present embodiment, can be using the laser point cloud of the barrier object on mobile lidar acquisition road.
Specifically, for vehicle obstacle recognition method operation thereon electronic equipment (such as vehicle driving electricity
Brain or car-mounted terminal) laser radar can be controlled by wired connection mode or radio connection.Specifically, in vehicle
In (such as pilotless automobile) driving process, car running computer or car-mounted terminal can control laser radar to preset frequency acquisition
The laser point cloud for acquiring the barrier object on the road of vehicle driving, to get the three-dimensional of the barrier object on road
Point cloud data.Wherein, the point spacing on identical barrier is small, and the point spacing between different barriers is big, as shown in Figure 2.
It should be pointed out that above-mentioned radio connection can include but is not limited to 3G/4G connection, WiFi connection, bluetooth
Connection, WiMAX connection, Zigbee connection, UWB (ultra wideband) connection and other currently known or exploitations in the future
Radio connection.
The specification of laser radar can use 16 lines, 32 lines or 64 lines, wherein the higher unit for indicating laser radar of line number
Energy density is bigger, and precision is higher.In the present embodiment, using 64 line laser radars, the laser radar is by 64 laser photosensitive two
Pole pipe composition detects ambient enviroment with 600r/min revolving speed, in vertical direction has 26 °~5 ° of measurement range, 100m away from
From the interior resolution ratio that can reach 5cm.
In some optional implementations of the present embodiment, barrier object includes: Vehicle Object, pedestrian's object.Vehicle
Object can include but is not limited to: bicycle, car, truck, minibus, bus.
In 102, the three dimensional point cloud is subjected to down-sampling, is projected on the two-dimensional grid parallel with ground, structure
Build two-dimensional grid image；Including following sub-step:
In 1021, cartesian coordinate is constructed；
Using laser radar as coordinate origin, using the vertical axis direction of laser radar as Z axis, using right ahead as X-axis,
Y-axis is to be determined by Z axis and X-axis according to right-hand screw rule, is vehicle left.
In 1022, three-dimensional point cloud is resolved to cartesian coordinate system；
Distance and space angle according to collected each laser scanning point away from laser emitter, calculating there emerged a laser scanning
Coordinate position of the point in cartesian coordinate system.For laser radar point cloud, original point cloud format is UDP network data
Packet needs to resolve its original point cloud into the cartesian coordinate system established.
During unmanned vehicle traveling, laser radar is simultaneously with certain angular speed uniform rotation, in this process
It constantly issues laser and collects the information of reflection point, to obtain comprehensive environmental information.Laser radar is collecting reflection
Also time and the level angle of point generation can be recorded during point distance simultaneously, and each laser emitter has volume
Number and fixed vertical angle, the coordinate of all reflection points can be calculated according to these data.Laser radar often rotates a circle
The set for all reflection point coordinates being collected into is formed a cloud.
Laser radar can measure the distance distance with object by laser reflection, because the vertical angle of laser is
Fixed, it is denoted as a, it is sin (a) * distance that we, which can directly find out z-axis coordinate, here.By cos (a) * distance I
Available distance in x, the projection of y plane is denoted as xy_dist.Laser radar record reflection point apart from while
Also the level angle b that will record lower present laser radar rotation, according to simple set conversion, the x-axis of the available point is sat
Mark and y-axis coordinate are respectively cos (b) * xy_dist and sin (b) * xy_dist.
Preferably, the three dimensional point cloud under cartesian coordinate system is pre-processed, reserved-range in -0m < X < 50m, -
Three dimensional point cloud within the scope of 25m < Y < 25m, -3m < Z < 3m.
In 1023, down-sampling is carried out to three-dimensional point cloud, is projected to along z-axis on the two-dimensional grid parallel with ground, is generated
Two-dimensional grid image；
X, y plane in cartesian coordinate system is divided into N number of grid, the resolution ratio of grid can according to the actual situation into
Row setting；In general, one grid corresponds to the square that actual size is 0.3 meter of side length in rasterizing.
Wherein, in the mean value (x_mean, y_mean) and the direction z in each grid record cylindricality on all the points x, y direction
Peak (z_max).Peak on this two-dimensional grid image recording z direction, referred to as height map.In this way, then existing
All objects being above the ground level are marked in grid, improve the robustness of detection.
Preferably, it during carrying out down-sampling to three-dimensional point cloud, stores between three-dimensional point cloud and the pixel of grid
Corresponding relationship.
Preferably, the value range for adjusting all grids simulates its linear stretch to 0-255 every with the value after stretching
The gray scale of a grid, and two dimensional gray grid image is generated with this.
In 103, the barrier in two-dimensional grid image is detected.
It is sparse discrete, a usual object (such as vehicle, pedestrian etc.) since 64 line laser radars obtain data
Many discrete fritters can be divided into, directly can not further be classified to barrier.Therefore, it is necessary to obtained
Grid image carries out correlation cluster, so that the discrete point in a barrier can flock together, to detect barrier,
Obtain the corresponding image of barrier to be identified.
In the present embodiment, specifically which kind of detection model to detect that barrier can basis from the two-dimensional grid image using
Depending on actual needs, for example, being split using region growing algorithm to two-dimensional grid image.
Specifically, including following sub-step:
Step 1031, randomly selected from two-dimensional grid image 1 not yet ownership pixel as seed point, if the picture
Element is (x0, y0), with stack representation seed set, by seed point (x0, y0) push into seed storehouse；
First seed point (x0, y0) pop in seed storehouse is gone out storehouse by step 1032, and centered on the point, traversal
8 neighborhood territory pixel (x, y) of center；
Whether judgement traversal pixel (x, y) is in seed region；If not, whether judgement traversal pixel (x, y) is full
Smoothness constraint condition between foot and seed point (x0, y0)；If traversal pixel (x, y) meets condition, by the traversal pixel
(x, y) push is into storehouse；Meanwhile cluster set is added in seed point (x0, y0)；
Step 1033 judges whether seed storehouse is empty；If so, by cluster gather in pixel constitute one
Cluster, the cluster are the corresponding pixel collection of barrier, execute step 1034；If not, executing step 1032；
Step 1034 judges whether all pixels were all once added into seed storehouse in two-dimensional grid image, if so,
Region increases and terminates, and exports all cluster；If not, executing step 1031.
All pixels belong to a determining cluster, and are not connected to mutually between these cluster.
Preferably, according to the corresponding relationship between three-dimensional point cloud and the pixel of grid, barrier corresponding three can be obtained
Dimension point cloud, thus, it is possible to avoid the missing inspection of obstacle object point cloud.
Further, it can be based on the corresponding three-dimensional point cloud of the barrier to be identified, identify the class of the barrier
Type.
Preferably, deep learning algorithm can be used, the type of barrier is identified.Which kind of deep learning algorithm specifically used
It can be determined according to actual needs, for example, can be used using wider convolutional neural networks (CNN, Convolution Neural
Network) algorithm.
Convolutional neural networks are a kind of multilayer neural networks, are good at the correlation machine study that processing image is especially big image
Problem.Convolutional neural networks finally make it successfully by the continuous dimensionality reduction of the huge problem of image recognition of data by serial of methods
It can be trained to.One typical convolutional neural networks can be made of, wherein convolutional layer convolutional layer, pond layer, full articulamentum
Cooperate with pond layer, form multiple convolution groups, successively extract feature, completes classification eventually by several full articulamentums.It is comprehensive
For, convolutional neural networks are distinguished by convolution come simulation feature, and the weight for passing through convolution is shared and pond, to reduce net
The order of magnitude of network parameter completes the tasks such as classification finally by traditional neural network.
Be labeled by the type to barrier, be sent to server, so as on the server be based on LMDB or
The two-dimensional grid image in the databases such as LEVELDB and the markup information generation training sample to barrier object.It can be with
Using the training sample to using the two-dimensional grid image as input, the convolutional Neural net that the classification of barrier is identified
Network is trained, and then obtains machine learning model.
Two-dimensional grid image to be identified is identified by the machine learning model after training, can determine obstacle
The type of object, such as people, bicycle, motor vehicle.
Fig. 3 be obstacle detection method of the present invention another embodiment flow chart, as shown in figure 3, include with
Lower step:
In 301, automatic driving vehicle ambient enviroment is scanned to obtain three-dimensional point cloud.
In 302, the three-dimensional point cloud is subjected to down-sampling, projected on 3D grid, construct 3D grid image；Including with
Lower sub-step:
In 3021, cartesian coordinate is constructed；
Using laser radar as coordinate origin, using the vertical axis direction of laser radar as Z axis, using right ahead as X-axis,
Y-axis is to be determined by Z axis and X-axis according to right-hand screw rule, is vehicle left.
In 3022, three-dimensional point cloud is resolved to cartesian coordinate system；
Distance and space angle according to collected each laser scanning point away from laser emitter, calculating there emerged a laser scanning
Coordinate position of the point in cartesian coordinate system.For laser radar point cloud, original point cloud format is UDP network data
Packet needs to resolve its original point cloud into the cartesian coordinate system established.
Preferably, the three dimensional point cloud under cartesian coordinate system is pre-processed, reserved-range in -0m < X < 50m, -
Three dimensional point cloud within the scope of 25m < Y < 25m, -3m < Z < 3m.
In 3023, down-sampling is carried out to three-dimensional point cloud, is projected on 3D grid, each grid, which records in the grid, to be owned
The mean value of point；
X, y, z space in cartesian coordinate system is divided into N number of grid, the resolution ratio of grid can be according to the actual situation
It is set；In general, one grid corresponds to the square that actual size is 0.3 meter of side length in rasterizing.
Wherein, each grid records the mean value (x_mean, y_mean, z_mean) of all the points in the grid.
Preferably, it during carrying out down-sampling to three-dimensional point cloud, stores between three-dimensional point cloud and the pixel of grid
Corresponding relationship.
Preferably, the value range for adjusting all grids, by its x, y, z value linear stretch to 0-255, with the value after stretching
The gray scale of each grid is simulated, and 3D gray scale grid image is generated with this.
Preferably, it due to the noise of laser radar itself or hanging small obstacle, is deposited in the three dimensional point cloud that can make
In a small amount of abnormal point, therefore progress single-point filters out before grid image output and vacantly point filters out, effectively to inhibit sensor
Noise and ambient noise interference.
In 303, the barrier in 3D grid image, including following sub-step are detected:
In the present embodiment, specifically which kind of detection model to detect barrier can be according to practical need using from 3D grid image
Depending on wanting, for example, being split using region growing algorithm to image.
Step 3031, randomly selected from 3D grid image 1 not yet ownership pixel as seed point, if the pixel
For (x0, y0), with stack representation seed set, by seed point (x0, y0) push into seed storehouse；
First seed point (x0, y0) pop in seed storehouse is gone out storehouse by step 3032, and centered on the point, traversal
26 neighborhood territory pixel (x, y) of center；
Whether judgement traversal pixel (x, y) is in seed region；If not, whether judgement traversal pixel (x, y) is full
Smoothness constraint condition between foot and seed point (x0, y0)；If traversal pixel (x, y) meets condition, by the traversal pixel
(x, y) push is into storehouse；Meanwhile cluster set is added in seed point (x0, y0)；Wherein, the smoothness constraint condition is
It traverses the distance between pixel (x, y) and seed point (x0, y0) and is less than " combined distance "；
Step 3033 judges whether seed storehouse is empty；If so, by cluster gather in pixel constitute one
Cluster, the cluster are the corresponding pixel collection of barrier, execute step 3034；If not, executing step 3032；
Step 3034 judges whether all pixels were all once added into seed storehouse in 3D grid image, if so, area
Domain increases and terminates, and exports all cluster；If not, executing step 3031.
All pixels belong to a determining cluster, and are not connected to mutually between these cluster.
Preferably, according to the corresponding relationship between three-dimensional point cloud and the pixel of grid, barrier corresponding three can be obtained
Dimension point cloud, thus, it is possible to avoid the missing inspection of obstacle object point cloud.
Preferably, it can be based on the corresponding three-dimensional point cloud of the barrier to be identified, identify the type of the barrier.
Fig. 4 be obstacle detection method of the present invention another embodiment flow chart, as shown in figure 4, include with
Lower step:
In 401, automatic driving vehicle ambient enviroment is scanned to obtain three-dimensional point cloud.
During unmanned vehicle traveling, laser radar is simultaneously with certain angular speed uniform rotation, in this process
It constantly issues laser and collects the information of reflection point, to obtain comprehensive environmental information.As laser radar
Scan characteristic, distance (depth) difference of adjacent laser scanning point to transmitter is smaller on same object, phase on different objects
Distance (depth) difference of adjacent laser scanning point to transmitter is larger.
In 402, by the three-dimensional point cloud carry out down-sampling, project before on the two-dimensional grid at visual angle, building before to
Visual angle two-dimensional grid image；Including following sub-step:
In 4021, cartesian coordinate is constructed；
Using laser radar as coordinate origin, using the vertical axis direction of laser radar as Z axis, using right ahead as X-axis,
Y-axis is to be determined by Z axis and X-axis according to right-hand screw rule, is vehicle left.
In 4022, three-dimensional point cloud is resolved to cartesian coordinate system；
Distance and space angle according to collected each laser scanning point away from laser emitter, calculating there emerged a laser scanning
Coordinate position of the point in cartesian coordinate system.For laser radar point cloud, original point cloud format is UDP network data
Packet needs to resolve its original point cloud into the cartesian coordinate system established.
Preferably, the three dimensional point cloud under cartesian coordinate system is pre-processed, reserved-range in -0m < X < 50m, -
Three dimensional point cloud within the scope of 25m < Y < 25m, -3m < Z < 3m.
In 4023, down-sampling is carried out to three-dimensional point cloud, before throwing by scanning sequency on the two-dimensional grid at visual angle, each
Grid records in the grid laser scanning point to distance (depth) information of laser emitter.
Preferably, it during carrying out down-sampling to three-dimensional point cloud, stores between three-dimensional point cloud and the pixel of grid
Corresponding relationship.
Preferably, the value range for adjusting all grids simulates its linear stretch to 0-255 every with the value after stretching
The gray scale of a grid, and with this generate before to visual angle two dimensional gray grid image.
The barrier in 403, before from detection to visual angle two-dimensional grid image；Including following sub-step:
In the present embodiment, specifically which kind of detection model to detect that barrier can from the two-dimensional grid image of forward direction visual angle using
It is decided according to the actual requirements, for example, being split to preceding to visual angle two-dimensional grid image using region growing algorithm.
Specifically, including following sub-step:
Step 4031,1 is randomly selected from the two-dimensional grid image of forward direction visual angle, and there are no the pixels of ownership as seed
Point, if the pixel is (x0, y0), with stack representation seed set, by seed point (x0, y0) push into seed storehouse；
First seed point (x0, y0) pop in seed storehouse is gone out storehouse by step 4032, and centered on the point, traversal
8 neighborhood territory pixel (x, y) of center；
Whether judgement traversal pixel (x, y) is in seed region；If not, whether judgement traversal pixel (x, y) is full
Smoothness constraint condition between foot and seed point (x0, y0)；If traversal pixel (x, y) meets condition, by the traversal pixel
(x, y) push is into storehouse；Meanwhile cluster set is added in seed point (x0, y0)；Wherein, the smoothness constraint condition is
The depth difference traversed between pixel (x, y) and seed point (x0, y0) is less than threshold value；
Step 4033 judges whether seed storehouse is empty；If so, by cluster gather in pixel constitute one
Cluster, the cluster are the corresponding pixel collection of barrier, execute step 4034；If not, executing step 4032；
Into visual angle two-dimensional grid image, whether all pixels were all once added into seed heap before step 4034, judgement
Stack exports all cluster if so, region, which increases, to terminate；If not, executing step 4031.
All pixels belong to a determining cluster, and are not connected to mutually between these cluster.
Preferably, according to the corresponding relationship between three-dimensional point cloud and the pixel of grid, barrier corresponding three can be obtained
Dimension point cloud, thus, it is possible to avoid the missing inspection of obstacle object point cloud.
Preferably, it can be based on the corresponding three-dimensional point cloud of the barrier to be identified, identify the type of the barrier.
Down-sampling has been carried out to initial three-dimensional point cloud three-dimensional point cloud；Grid image is split using region growing algorithm
It is highly efficient compared to being split on initial three-dimensional point cloud three-dimensional point cloud, because, it is only necessary to it calculates in each neighborhood of pixel points
Other points do not need on three-dimensional point cloud three-dimensional point cloud with k-d tree (k-dimensional tree) removal search Neighbor Points；Drop
Low data processing amount, improves real-time.
By the way that the three-dimensional point cloud is carried out down-sampling, project on the two-dimensional grid parallel with ground, generation has recorded z
The two-dimensional grid image of peak on direction, improves the robustness of detection.
The introduction about embodiment of the method above, below by way of Installation practice, to scheme of the present invention carry out into
One step explanation.
Fig. 5 is the composed structure schematic diagram of obstacle detector embodiment of the present invention, as shown in Figure 5, comprising: obtain
Take unit 51, projecting unit 52 and detection unit 53.
The acquiring unit, for being scanned to obtain three-dimensional point cloud to automatic driving vehicle ambient enviroment；
The map unit projects on grid for the three-dimensional point cloud to be carried out down-sampling, constructs grid image；
The taxon, for detecting the barrier in the grid image.
Preferably, the acquiring unit is used to acquire swashing for the barrier object on road in advance using mobile lidar
Luminous point cloud.
The specification of laser radar can use 16 lines, 32 lines or 64 lines, wherein the higher unit for indicating laser radar of line number
Energy density is bigger, and precision is higher.In the present embodiment, using 64 line laser radars, the laser radar is by 64 laser photosensitive two
Pole pipe composition detects ambient enviroment with 600r/min revolving speed, in vertical direction has 26 °~5 ° of measurement range, 100m away from
From the interior resolution ratio that can reach 5cm.
In some optional implementations of the present embodiment, barrier object includes: Vehicle Object, pedestrian's object.Vehicle
Object can include but is not limited to: bicycle, car, truck, minibus, bus.
Preferably, the map unit is used to the three-dimensional point cloud carrying out down-sampling, projects two parallel with ground
It ties up on grid, generates two-dimensional grid image；The map unit specifically executes:
Construct cartesian coordinate；
Three-dimensional point cloud is resolved to cartesian coordinate system；
Down-sampling is carried out to three-dimensional point cloud, is projected to along z-axis on the two-dimensional grid parallel with ground, two-dimensional mesh trrellis diagram is generated
Picture；Wherein, the peak in the mean value and the direction z in each grid record cylindricality on all the points x, y direction.
Preferably, it during carrying out down-sampling to three-dimensional point cloud, stores between three-dimensional point cloud and the pixel of grid
Corresponding relationship.
The three-dimensional point cloud is subjected to down-sampling, is projected on 3D grid, 3D grid image is constructed；The map unit tool
Body executes:
Construct cartesian coordinate；
Three-dimensional point cloud is resolved to cartesian coordinate system；
Down-sampling is carried out to three-dimensional point cloud, is projected on 3D grid, each grid records the mean value of all the points in the grid.
Preferably, it during carrying out down-sampling to three-dimensional point cloud, stores between three-dimensional point cloud and the pixel of grid
Corresponding relationship.
Preferably, the map unit for the map unit before projecting to the two-dimensional grid at visual angle, building
Forward direction visual angle two-dimensional grid image；The map unit specifically executes:
Construct cartesian coordinate；
Three-dimensional point cloud is resolved to cartesian coordinate system；
Down-sampling is carried out to three-dimensional point cloud, before being thrown by scanning sequency on the two-dimensional grid at visual angle, each grid record
Depth information of the laser scanning point to laser emitter in the grid.
Preferably, it during carrying out down-sampling to three-dimensional point cloud, stores between three-dimensional point cloud and the pixel of grid
Corresponding relationship.
Preferably, the taxon, for carrying out detection of obstacles to the grid image using region growing algorithm,
Detect the barrier in the grid image.
The taxon is specifically held when using area growth algorithm carries out detection of obstacles to the grid image
Row:
Sub-step 1, randomly selected from grid image 1 not yet ownership pixel as seed point, if the pixel is
(x0, y0), with stack representation seed set, by seed point (x0, y0) push into seed storehouse；
First seed point (x0, y0) pop in seed storehouse is gone out storehouse by sub-step 2, and centered on the point, traversal
The center neighborhood territory pixel (x, y)；
Whether judgement traversal pixel (x, y) is in seed region；If not, whether judgement traversal pixel (x, y) is full
Smoothness constraint condition between foot and seed point (x0, y0)；If traversal pixel (x, y) meets condition, by the traversal pixel
(x, y) push is into storehouse；Meanwhile cluster set is added in seed point (x0, y0)；
Sub-step 3 judges whether seed storehouse is empty；If so, by cluster gather in pixel constitute one
Cluster, the cluster are the corresponding pixel collection of barrier, execute sub-step 4；If not, executing sub-step 2；
Sub-step 4 judges whether all pixels were all once added into seed storehouse in 2-D gray image, if so,
Region increases and terminates, and exports all cluster；If not, executing sub-step 1.
Preferably, according to the corresponding relationship between three-dimensional point cloud and the pixel of grid, barrier corresponding three can be obtained
Dimension point cloud, thus, it is possible to avoid the missing inspection of obstacle object point cloud.
Preferably, it can be based on the corresponding three-dimensional point cloud of the barrier to be identified, identify the type of the barrier.
The specific workflow of Fig. 5 shown device embodiment please refers to the related description in preceding method embodiment, no longer
It repeats.
It can be seen that based on above-mentioned introduction using mode described in above-described embodiment, down-sampling, projection carried out to three-dimensional point cloud
Onto grid, grid image is constructed, detects the barrier in the grid image using region growing algorithm.It can be to all barriers
Hinder object all to complete to divide, barrier will not be missed, additionally it is possible to meet the needs of real-time.
Fig. 6 shows the frame for being suitable for the exemplary computer system/server 012 for being used to realize embodiment of the present invention
Figure.The computer system/server 012 that Fig. 6 is shown is only an example, should not function and use to the embodiment of the present invention
Range band carrys out any restrictions.
As shown in fig. 6, computer system/server 012 is showed in the form of universal computing device.Computer system/clothes
The component of business device 012 can include but is not limited to: one or more processor or processing unit 016, system storage
028, connect the bus 018 of different system components (including system storage 028 and processing unit 016).
Bus 018 indicates one of a few class bus structures or a variety of, including memory bus or Memory Controller,
Peripheral bus, graphics acceleration port, processor or the local bus using any bus structures in a variety of bus structures.It lifts
For example, these architectures include but is not limited to industry standard architecture (ISA) bus, microchannel architecture (MAC)
Bus, enhanced isa bus, Video Electronics Standards Association (VESA) local bus and peripheral component interconnection (PCI) bus.
Computer system/server 012 typically comprises a variety of computer system readable media.These media, which can be, appoints
The usable medium what can be accessed by computer system/server 012, including volatile and non-volatile media, movably
With immovable medium.
System storage 028 may include the computer system readable media of form of volatile memory, such as deposit at random
Access to memory (RAM) 030 and/or cache memory 032.Computer system/server 012 may further include other
Removable/nonremovable, volatile/non-volatile computer system storage medium.Only as an example, storage system 034 can
For reading and writing immovable, non-volatile magnetic media (Fig. 6 do not show, commonly referred to as " hard disk drive ").Although in Fig. 6
It is not shown, the disc driver for reading and writing to removable non-volatile magnetic disk (such as " floppy disk ") can be provided, and to can
The CD drive of mobile anonvolatile optical disk (such as CD-ROM, DVD-ROM or other optical mediums) read-write.In these situations
Under, each driver can be connected by one or more data media interfaces with bus 018.Memory 028 may include
At least one program product, the program product have one group of (for example, at least one) program module, these program modules are configured
To execute the function of various embodiments of the present invention.
Program/utility 040 with one group of (at least one) program module 042, can store in such as memory
In 028, such program module 042 includes --- but being not limited to --- operating system, one or more application program, other
It may include the realization of network environment in program module and program data, each of these examples or certain combination.Journey
Sequence module 042 usually executes function and/or method in embodiment described in the invention.
Computer system/server 012 can also with one or more external equipments 014 (such as keyboard, sensing equipment,
Display 024 etc.) communication, in the present invention, computer system/server 012 is communicated with outside radar equipment, can also be with
One or more enable a user to the equipment interacted with the computer system/server 012 communication, and/or with make the meter
Any equipment (such as network interface card, the modulation that calculation machine systems/servers 012 can be communicated with one or more of the other calculating equipment
Demodulator etc.) communication.This communication can be carried out by input/output (I/O) interface 022.Also, computer system/clothes
Being engaged in device 012 can also be by network adapter 020 and one or more network (such as local area network (LAN), wide area network (WAN)
And/or public network, such as internet) communication.As shown, network adapter 020 by bus 018 and computer system/
Other modules of server 012 communicate.It should be understood that computer system/server 012 can be combined although being not shown in Fig. 6
Using other hardware and/or software module, including but not limited to: microcode, device driver, redundant processing unit, external magnetic
Dish driving array, RAID system, tape drive and data backup storage system etc..
The program that processing unit 016 is stored in system storage 028 by operation, thereby executing described in the invention
Function and/or method in embodiment.
Above-mentioned computer program can be set in computer storage medium, i.e., the computer storage medium is encoded with
Computer program, the program by one or more computers when being executed, so that one or more computers execute in the present invention
State method flow shown in embodiment and/or device operation.
With time, the development of technology, medium meaning is more and more extensive, and the route of transmission of computer program is no longer limited by
Tangible medium, can also be directly from network downloading etc..It can be using any combination of one or more computer-readable media.
Computer-readable medium can be computer-readable signal media or computer readable storage medium.Computer-readable storage medium
Matter for example may be-but not limited to-system, device or the device of electricity, magnetic, optical, electromagnetic, infrared ray or semiconductor, or
Any above combination of person.The more specific example (non exhaustive list) of computer readable storage medium includes: with one
Or the electrical connections of multiple conducting wires, portable computer diskette, hard disk, random access memory (RAM), read-only memory (ROM),
Erasable programmable read only memory (EPROM or flash memory), optical fiber, portable compact disc read-only memory (CD-ROM), light
Memory device, magnetic memory device or above-mentioned any appropriate combination.In this document, computer readable storage medium can
With to be any include or the tangible medium of storage program, the program can be commanded execution system, device or device use or
Person is in connection.
Computer-readable signal media may include in a base band or as carrier wave a part propagate data-signal,
Wherein carry computer-readable program code.The data-signal of this propagation can take various forms, including --- but
It is not limited to --- electromagnetic signal, optical signal or above-mentioned any appropriate combination.Computer-readable signal media can also be
Any computer-readable medium other than computer readable storage medium, which can send, propagate or
Transmission is for by the use of instruction execution system, device or device or program in connection.
The program code for including on computer-readable medium can transmit with any suitable medium, including --- but it is unlimited
In --- wireless, electric wire, optical cable, RF etc. or above-mentioned any appropriate combination.
The computer for executing operation of the present invention can be write with one or more programming languages or combinations thereof
Program code, described program design language include object oriented program language-such as Java, Smalltalk, C++,
It further include conventional procedural programming language-such as " C " language or similar programming language.Program code can be with
It fully executes, partly execute on the user computer on the user computer, being executed as an independent software package, portion
Divide and partially executes or executed on a remote computer or server completely on the remote computer on the user computer.?
Be related in the situation of remote computer, remote computer can pass through the network of any kind --- including local area network (LAN) or
Wide area network (WAN) is connected to subscriber computer, or, it may be connected to outer computer (such as provided using Internet service
Quotient is connected by internet).
It is apparent to those skilled in the art that for convenience and simplicity of description, the system of the description,
The specific work process of device and unit, can refer to corresponding processes in the foregoing method embodiment, and details are not described herein.
In several embodiments provided herein, it should be understood that disclosed method and apparatus can pass through it
Its mode is realized.For example, the apparatus embodiments described above are merely exemplary, for example, the division of the unit, only
Only a kind of logical function partition, there may be another division manner in actual implementation, such as multiple units or components can be tied
Another system is closed or is desirably integrated into, or some features can be ignored or not executed.Another point, it is shown or discussed
Mutual coupling, direct-coupling or communication connection can be through some interfaces, the INDIRECT COUPLING or logical of device or unit
Letter connection can be electrical property, mechanical or other forms.
The unit as illustrated by the separation member may or may not be physically separated, aobvious as unit
The component shown may or may not be physical unit, it can and it is in one place, or may be distributed over multiple
In network unit.It can select some or all of unit therein according to the actual needs to realize the mesh of this embodiment scheme
's.
It, can also be in addition, each functional unit in each embodiment of the application can integrate in one processing unit
It is that each unit physically exists alone, can also be integrated in one unit with two or more units.The integrated list
Member both can take the form of hardware realization, can also realize in the form of hardware adds SFU software functional unit.
Finally, it should be noted that above embodiments are only to illustrate the technical solution of the application, rather than its limitations；Although
The application is described in detail with reference to the foregoing embodiments, those skilled in the art should understand that: it still may be used
To modify the technical solutions described in the foregoing embodiments or equivalent replacement of some of the technical features；
And these are modified or replaceed, each embodiment technical solution of the application that it does not separate the essence of the corresponding technical solution spirit and
Range.
Claims (18)
1. a kind of obstacle detection method characterized by comprising
Automatic driving vehicle ambient enviroment is scanned to obtain three-dimensional point cloud；
The three-dimensional point cloud is subjected to down-sampling, is projected on grid, grid image is constructed；
Detect the barrier in the grid image.
2. being projected the method according to claim 1, wherein described carry out down-sampling for the three-dimensional point cloud
Further include: on grid
The three-dimensional point cloud is subjected to down-sampling, is projected on the two-dimensional grid parallel with ground；Or,
The three-dimensional point cloud is subjected to down-sampling, is projected on 3D grid；Or,
The three-dimensional point cloud is subjected to down-sampling, before projecting on the two-dimensional grid at visual angle.
3. the method according to claim 1, wherein the barrier in the detection grid image is further
Include:
Detection of obstacles is carried out to the grid image using region growing algorithm.
4. according to the method described in claim 2, it is characterized in that, it is described by the three-dimensional point cloud carry out down-sampling, project
Further include: on parallel two-dimensional grid with ground
The peak in mean value and the direction z in each grid record cylindricality on all the points x, y direction.
5. according to the method described in claim 2, it is characterized in that, described will carry out the three-dimensional point cloud down-sampling, projection
Further include: on to 3D grid
Each grid records the mean value of all the points in the grid.
6. according to the method described in claim 2, it is characterized in that, it is described by the three-dimensional point cloud carry out down-sampling, project
Further include: on the two-dimensional grid at forward direction visual angle
Each grid records in the grid laser scanning point to the depth information of laser emitter.
7. according to the method described in claim 3, it is characterized in that, grid image described in the using area growth algorithm carries out
Detection of obstacles, including following sub-step:
Sub-step 1, randomly selected from grid image 1 not yet ownership pixel as seed point, if the pixel for (x0,
Y0), with stack representation seed set, by seed point (x0, y0) push into seed storehouse；
First seed point (x0, y0) pop in seed storehouse is gone out storehouse by sub-step 2, and centered on the point, traverses center
Neighborhood territory pixel (x, y)；
Whether judgement traversal pixel (x, y) is in seed region；If not, judgement traversal pixel (x, y) whether meet with
Smoothness constraint condition between seed point (x0, y0)；If traversal pixel (x, y) meets condition, by the traversal pixel (x, y)
Push is into storehouse；Meanwhile cluster set is added in seed point (x0, y0)；
Sub-step 3 judges whether seed storehouse is empty；If so, by cluster gather in pixel constitute a cluster,
The cluster is the corresponding pixel collection of barrier, executes sub-step 4；If not, executing sub-step 2；
Sub-step 4 judges whether all pixels were all once added into seed storehouse in 2-D gray image, if so, region
Growth terminates, and exports all cluster；If not, executing sub-step 1.
8. the method according to the description of claim 7 is characterized in that grid image described in the using area growth algorithm carries out
Detection of obstacles further comprises:
According to the corresponding relationship between the pixel of the three-dimensional point cloud and grid that store during down-sampling, it is corresponding to obtain barrier
Three-dimensional point cloud.
9. a kind of obstacle detector characterized by comprising acquiring unit, projecting unit and detection unit；
The acquiring unit, for being scanned to obtain three-dimensional point cloud to automatic driving vehicle ambient enviroment；
The map unit projects on grid for the three-dimensional point cloud to be carried out down-sampling, constructs grid image；
The taxon, for detecting the barrier in the grid image.
10. device according to claim 9, which is characterized in that the map unit is specifically used for:
The three-dimensional point cloud is subjected to down-sampling, is projected on the two-dimensional grid parallel with ground；Or,
The three-dimensional point cloud is subjected to down-sampling, is projected on 3D grid；Or,
The three-dimensional point cloud is subjected to down-sampling, before projecting on the two-dimensional grid at visual angle.
11. device according to claim 9, which is characterized in that the taxon is specifically used for:
Detection of obstacles is carried out to the grid image using region growing algorithm.
12. device according to claim 10, which is characterized in that the map unit is in the case where carrying out the three-dimensional point cloud
Sampling, specific to execute when projecting on the two-dimensional grid parallel with ground:
The peak in mean value and the direction z in cylindricality on all the points x, y direction is recorded in each grid.
13. device according to claim 10, which is characterized in that the map unit is in the case where carrying out the three-dimensional point cloud
Sampling, specific to execute when projecting on 3D grid:
Each grid records the mean value of all the points in the grid.
14. device according to claim 10, which is characterized in that the map unit is in the case where carrying out the three-dimensional point cloud
Sampling, specific to execute when before projecting on the two-dimensional grid at visual angle:
Each grid records in the grid laser scanning point to the depth information of laser emitter.
15. device according to claim 11, which is characterized in that the taxon is in using area growth algorithm to institute
It is specific to execute when stating grid image progress detection of obstacles:
Sub-step 1, randomly selected from grid image 1 not yet ownership pixel as seed point, if the pixel for (x0,
Y0), with stack representation seed set, by seed point (x0, y0) push into seed storehouse；
First seed point (x0, y0) pop in seed storehouse is gone out storehouse by sub-step 2, and centered on the point, traverses center
Neighborhood territory pixel (x, y)；
Whether judgement traversal pixel (x, y) is in seed region；If not, judgement traversal pixel (x, y) whether meet with
Smoothness constraint condition between seed point (x0, y0)；If traversal pixel (x, y) meets condition, by the traversal pixel (x, y)
Push is into storehouse；Meanwhile cluster set is added in seed point (x0, y0)；
Sub-step 3 judges whether seed storehouse is empty；If so, by cluster gather in pixel constitute a cluster,
The cluster is the corresponding pixel collection of barrier, executes sub-step 4；If not, executing sub-step 2；
Sub-step 4 judges whether all pixels were all once added into seed storehouse in 2-D gray image, if so, region
Growth terminates, and exports all cluster；If not, executing sub-step 1.
16. device according to claim 11, which is characterized in that the taxon is in using area growth algorithm to institute
It is specific to execute when stating grid image progress detection of obstacles:
According to the corresponding relationship between the pixel of the three-dimensional point cloud and grid that store during down-sampling, it is corresponding to obtain barrier
Three-dimensional point cloud.
17. a kind of computer equipment, including memory, processor and it is stored on the memory and can be on the processor
The computer program of operation, which is characterized in that the processor is realized when executing described program as any in claim 1~8
Method described in.
18. a kind of computer readable storage medium, is stored thereon with computer program, which is characterized in that described program is processed
Such as method according to any one of claims 1 to 8 is realized when device executes.
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