CN111537994B - Unmanned mine card obstacle detection method - Google Patents
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Abstract
The invention discloses a method for detecting an unmanned mine card obstacle, which works according to the following steps: respectively converting barrier data acquired by a laser radar and a millimeter wave radar into corresponding vehicle body coordinate systems; drawing a 0 and 1 binary image of a ground-elevated point by combining the height difference of the grid map with the ground detection of the neighborhood difference value; clustering elevated points by adopting a multi-parameter model; judging whether the clustering result is an obstacle influencing the normal running of the vehicle or not according to the motion track of the vehicle; detecting whether the vehicle is in a drivable area; and matching the obstacle data acquired by the millimeter wave radar with the obstacle data acquired by the laser radar, and outputting a final result. The method provided by the invention is used for effectively detecting the obstacles in the road aiming at the practical application environment of the mining dump truck, so that the missing detection is prevented, and the clustering is accurate. The invention has better robustness, and reduces the false detection rate by adopting a scheme of fusing various radars and matching the detection result of the laser radar with the detection result of the millimeter wave radar.
Description
Technical Field
The invention relates to the technical field of unmanned mine card driving, in particular to a method for detecting obstacles of an unmanned mine card.
Background
The mine scene operation is single, the scene is relatively simple, and the optimal scene for the unmanned technology to land on the ground is provided. Compared with an urban road for detecting mine scene obstacles, the urban road is mainly characterized in that: the road is rugged, and the mine truck is always in a bumpy state in the running process, so that the data acquisition of the sensor is influenced; the road barriers can be fallen rocks and the like besides vehicles and pedestrians, and a sensing system needs to accurately detect the barriers with certain sizes; the road has no obvious road edge, the two sides of the road mainly comprise trees, weeds and soil slopes, and the detection of the drivable area is difficult. In addition, in a mine scene, the design of the sensing system needs to consider the severe conditions such as rain, snow, strong and weak light, dust raising and the like, and higher requirements are put on the detection of the obstacles.
In the prior art, a monitoring method for an unmanned mine card obstacle is available, and a patent with application number 201610687204.6 discloses a rapid obstacle detection method, aiming at a general vehicle, a method of point cloud grid map and internal height difference is adopted to detect the obstacle, so that the condition of missed detection is easy to occur; a template matching method is adopted to cluster various obstacles on a road, but in a three-dimensional scene, the problem of inaccurate matching calculation between a clustering result and a template can occur due to the fact that the relative position of a laser radar and a target changes constantly. Application number 201110150818.8 discloses a method for detecting an unstructured road boundary by combining an SVM (support vector machine) and a laser radar, which is incomplete in detection of a structured road, and the safety of the unstructured road cannot be guaranteed due to the lack of obvious road edge information.
Disclosure of Invention
In order to overcome the defects in the prior art, the invention provides a method for detecting obstacles of an unmanned mine card, and solves the technical problems that the detection of obstacles of the unmanned mine card is not strong in applicable scene capability and poor in robustness in the prior art.
In order to achieve the above purpose, the invention adopts the following technical scheme:
a method for detecting obstacles of an unmanned mine card is characterized by comprising the following steps: the method comprises the following steps:
respectively converting barrier data acquired by a laser radar and a millimeter wave radar into corresponding vehicle body coordinate systems;
drawing a 0 and 1 binary image of a ground-elevated point by combining the height difference of the grid map with the ground detection of the neighborhood difference value;
clustering elevated points by adopting a multi-parameter model;
judging whether the clustering result is an obstacle influencing the normal running of the vehicle or not according to the motion track of the vehicle;
detecting whether the vehicle is in a drivable area;
and matching the obstacle data acquired by the millimeter wave radar with the obstacle data acquired by the laser radar, and outputting a final result.
As a preferred embodiment of the present invention, the method for detecting an obstacle without a mine card comprises: the method for drawing the 0 and 1 binary image of the ground-elevated point by combining the height difference of the grid map with the ground detection of the neighborhood difference value comprises the following steps: the method comprises the steps of rasterizing barrier data, obtaining a grid diagram of N x N, calculating a difference value between the maximum value and the minimum value of point cloud elevation in each grid, carrying out gradient calculation on the difference value of each grid, comparing the difference value with a preset threshold value, identifying points with the difference value larger than the threshold value as 'elevated points', carrying out neighborhood difference calculation on point clouds with the difference value smaller than the threshold value, separating the 'elevated points' grid and ground grids, and fitting all ground grids into a road surface.
As a preferred embodiment of the present invention, the method for detecting an obstacle without a mine card comprises: the neighborhood in the neighborhood difference calculation is selected by considering grids within 2 meters around the grid as the neighborhood.
As a preferred embodiment of the present invention, the method for detecting an obstacle without a mine card comprises: the method for separating the ground grid comprises the following steps: dividing all point clouds belonging to the ground into a plurality of point cloud intervals according to the distribution of the vehicle distances, performing plane fitting on the point cloud intervals, and connecting all fitted planes to obtain the road surface.
As a preferable aspect of the present invention, the method for detecting an obstacle of an unmanned mine card comprises: and clustering the elevated points by using the multi-parameter model by adopting an eight-connected domain method to obtain elevated point cluster clusters, and merging a plurality of elevated point cluster clusters into one according to merging parameters to finish the target extraction of the point cloud.
As a preferred embodiment of the present invention, the method for detecting an obstacle without a mine card comprises: the merging parameters comprise the size and the density of the overhead point cluster.
As a preferred embodiment of the present invention, the method for detecting an obstacle without a mine card comprises: and converting the clustered elevated point cluster into two-dimensional data, calculating convex hull information of the two-dimensional data, and judging whether the safe driving of the vehicle is influenced or not according to the convex hull information and the driving track of the vehicle.
As a preferred embodiment of the present invention, the method for detecting an obstacle without a mine card comprises: and the left and right sides of the driving track of the vehicle are respectively extended by a certain width to be used as a safety zone of the vehicle, and if the safety zone has the elevated point cluster, the obstacle is determined to exist in the safety zone.
As a preferred embodiment of the present invention, the method for detecting an obstacle without a mine card comprises: the method for detecting whether the vehicle is in the drivable region comprises the following steps: selecting a certain point in front of the vehicle as a seed point, adopting a four-neighborhood growing mode, reserving a non-obstacle area in front of the mine card, calculating a distance value between a center point in front of the vehicle and the non-obstacle area, sequencing the distance value data to obtain a minimum value, comparing the minimum value with a set threshold value, if the minimum value is smaller than the set threshold value, adjusting the vehicle, otherwise, determining that the vehicle is safe.
As a preferred embodiment of the present invention, the method for detecting an obstacle without a mine card comprises: and selecting the center point in front of the vehicle as a seed point.
The invention achieves the following beneficial effects:
compared with the prior art, the invention provides the obstacle detection system of the automatic driving mining dump truck aiming at the practical application environment of the mining dump truck, so that the obstacles in the road are effectively detected, the detection omission is prevented, and the accurate clustering is realized; the method can effectively detect the road edge, ensures the safety, and is particularly suitable for unstructured road environments such as mines and the like. And the method is also suitable for severe conditions such as rain, snow, strong and weak light, dust raising and the like, and has good robustness. From the perspective of an applicable scene, the detection target of the method comprises a structured road besides a mine scene (unstructured road), and the mine card is prevented from entering a space except the road under the condition that the positioning information is invalid.
The invention has better robustness, and adopts a scheme of fusing various radars according to the working principle and the advantages and the disadvantages of the laser radar, and reduces the false detection rate by matching the detection result of the laser radar with the detection result of the millimeter wave radar. If the matching is successful, the laser radar detection result is saved as a final detection result, information such as the distance, the size, the orientation and the like of the obstacle is reserved, if the matching is failed, the information which is failed in the laser radar obstacle is filtered, the information which is successfully matched is reserved, and then the information of the successfully matched effective obstacle is output.
Drawings
FIG. 1 is an overall workflow diagram of the present invention;
FIG. 2 is a flow chart of the ground detection of the system of the present invention;
FIG. 3 is a flow chart of road information acquisition according to the present invention;
Detailed Description
The invention is further described below with reference to the accompanying drawings. The following examples are only for illustrating the technical solutions of the present invention more clearly, and the protection scope of the present invention is not limited thereby.
The embodiment discloses a method for detecting an obstacle without an ore card, which comprises the following specific steps of:
and respectively converting the obstacle data acquired by the laser radar and the millimeter wave radar into corresponding vehicle body coordinate systems. Since there are many types of lidar, and there may be more than one of each, multi-sensor data fusion is required.
And the ground data is detected by fitting the road surface by adopting a plurality of planes and combining the height difference of the grid map with the detection of the neighborhood difference. The traditional laser radar processing scheme needs to separate ground points from elevated points. On a structured road (mainly comprising an expressway and an urban arterial road), the road surface can be approximate to a plane, and the road surface is extracted by a plane extraction method. On an unstructured road, the road is rugged, and a plurality of planes are adopted to fit the road surface. And the height difference of the grid map is combined with the detection method of the neighborhood difference, but the method of extracting through the plane in the prior art has larger errors.
In the embodiment, the grid map height difference and neighborhood difference detection method is adopted, so that the ground can be stably and effectively detected.
Step 1, drawing a 0 and 1 binary image of ground-elevated points by combining grid map height difference with neighborhood difference ground detection. Firstly, rasterizing the laser radar point cloud data to obtain a grid map of N x N; then calculating the difference between the maximum value and the minimum value of the point cloud elevation in each grid, calculating the gradient of the point cloud elevation, comparing the difference A with a preset threshold D1, filtering out points (A is larger than D1) with the difference larger than the threshold as 'overhead points', reserving points (A is smaller than D1) with the difference smaller than the threshold, considering the points (A is smaller than D1) with the difference smaller than the threshold as the ground, performing neighborhood difference calculation on the points (A is smaller than D1) with the difference smaller than the threshold, and selecting the neighborhood related to the size of the grid, wherein the selection principle of the embodiment is to consider the grid within 2 meters around the grid as 'the neighborhood'; and dividing the point clouds belonging to the ground into a plurality of point cloud intervals according to the vehicle distance distribution, performing plane fitting on the point cloud intervals, and connecting the fitted planes to obtain the road surface. Thus, a 0, 1 binary map of the ground-overhead point is plotted.
And secondly, clustering the elevated points by adopting a multi-parameter model. And clustering the 0 and 1 binary images of the ground-elevated points by adopting an eight-connected domain method to obtain elevated point cluster clusters. Due to the fact that the mine scene mine card is large in size and has the characteristic that the outer contour is not a standard rectangle, the situations that point clouds are sparse, target feature points are shielded by the laser radar and a single target is clustered into a plurality of clusters after clustering can occur during laser radar detection. And combining a plurality of clusters into one by adopting a post-processing method of a multi-parameter model to finish the target extraction of the point cloud. Different merging parameters are set according to the size and the density of the clusters, and the clusters with different sizes are merged into one cluster according to the parameters, so that the problem of clustering inaccuracy caused by point cloud sparseness or target irregularity is solved.
Thirdly, detecting and judging the obstacles in the safety detection area based on the vehicle target driving route: converting the clustered clusters into two-dimensional data, calculating convex hull (obstacle) information of the two-dimensional data, and judging whether the two-dimensional data is an obstacle or not according to the convex hull information;
the running track of the vehicle is led into the system, and the left and the right of the running track are respectively expanded by certain width to be used as a safety area for the running of the mine card. And calculating the distance between each convex wrap point and the driving track, and judging whether the cluster is in the safety detection area of the mine card or not according to the distance value and the width of the safety area. If one convex package point distance value is smaller than the width of the safety zone, the obstacle is considered to be in the safety zone, otherwise, the obstacle is not in the safety zone.
And fourthly, detecting whether the vehicle is in the travelable area. As shown in fig. 3: in order to ensure the safe driving of the mine card, the edge of the road must be effectively detected, and the mine card is prevented from driving into the space outside the road under the condition that the positioning information is invalid.
A travelable region detection method based on a region growing algorithm is adopted. For a 0 and 1 binary image of a ground-elevated point, selecting a central point in front of the mine truck as a seed point, and extracting a non-obstacle area in front of the mine truck by adopting a four-neighborhood growing mode to obtain a driving area. And calculating the distance value between the center point in front of the mine card and the boundary point of the travelable area, sequencing the distance value data to obtain the minimum value, comparing the minimum value with a preset threshold value, and if the minimum value is smaller than the preset threshold value, the vehicle is in danger of touching the obstacle, otherwise, the vehicle is considered not to touch the obstacle in the travelable area.
And fifthly, matching the laser radar data with the millimeter wave radar data based on the matching distance threshold value, and outputting a final result. Laser radar's advantage lies in that its detection scope is wider, and detection precision is higher, but the performance is relatively poor under extreme weather such as sleet fog, and millimeter wave radar's penetration ability is strong, consequently for the accuracy nature and the security that promote system detection, this embodiment has still adopted millimeter wave radar data to match, specifically as follows:
firstly, the millimeter wave radar data is converted into a vehicle body coordinate system,
and accessing the millimeter wave radar into a noise removal filter, and performing noise removal and tracking processing on a target detected by the millimeter wave radar. The millimeter wave radar is not affected by rain, snow, dust and other conditions, so that the false detection rate is reduced by matching the detection result of the laser radar with the detection result of the millimeter wave radar. The matching method comprises the following steps: respectively judging whether the millimeter wave radar detection result is matched with the laser radar detection result according to the distance, dynamically distributing the distance threshold value through the dimension information of the obstacle detected by the laser radar, if the matching is successful, saving the laser radar detection result as a final detection result, keeping the distance, the size and the orientation information of the obstacle, if the matching is failed, filtering out the matching failure in the laser radar obstacle, keeping the matching success, and then outputting the effective obstacle information which is successfully matched.
In the aspect of hardware, one 32-line laser radar, three millimeter wave radars and one inertial navigation system can be selected.
Compared with the prior art, the invention provides the obstacle detection system of the automatic driving mining dump truck aiming at the practical application environment of the mining dump truck, so that the obstacles in the road are effectively detected, the detection omission is prevented, and the accurate clustering is realized; the method can effectively detect the road edge, ensures the safety and is particularly suitable for unstructured road environments such as mines and the like. And the method is also suitable for severe conditions such as rain, snow, strong and weak light, dust raising and the like, and has good robustness. From the perspective of an applicable scene, the detection target of the method comprises a structured road besides a mine scene (unstructured road), and the mine card is prevented from entering a space except the road under the condition that the positioning information is invalid.
The invention has better robustness, and adopts a scheme of fusing various radars according to the working principle and the advantages and the disadvantages of the laser radar, and reduces the false detection rate by matching the detection result of the laser radar with the detection result of the millimeter wave radar. If the matching is successful, the laser radar detection result is saved as a final detection result, information such as the distance, the size, the orientation and the like of the obstacle is reserved, if the matching is failed, the information which is failed in the laser radar obstacle is filtered, the information which is successfully matched is reserved, and then the information of the successfully matched effective obstacle is output.
The above description is only a preferred embodiment of the present invention, and it should be noted that, for those skilled in the art, several modifications and variations can be made without departing from the technical principle of the present invention, and these modifications and variations should also be regarded as the protection scope of the present invention.
Claims (7)
1. A method for detecting obstacles of an unmanned mine card is characterized by comprising the following steps: the method comprises the following steps:
respectively converting barrier data acquired by a laser radar and a millimeter wave radar into corresponding vehicle body coordinate systems;
drawing a 0 and 1 binary image of a ground-elevated point by combining the height difference of the grid map with the ground detection of the neighborhood difference value; the method specifically comprises the following steps: the method comprises the steps of rasterizing barrier data, obtaining a grid graph of N x N, calculating the difference value between the maximum value and the minimum value of point cloud elevation in each grid, performing gradient calculation on the difference value of each grid, comparing the difference value with a preset threshold value, identifying the point with the difference value larger than the threshold value as an elevated point, performing neighborhood difference calculation on the point cloud with the difference value smaller than the threshold value, separating the elevated point grid and a ground grid, and fitting all the ground grids into a road surface;
clustering elevated points by adopting a multi-parameter model;
judging whether the clustering result is an obstacle influencing the normal running of the vehicle or not according to the motion track of the vehicle; the method specifically comprises the following steps: guiding the driving track of the vehicle into a system, and expanding the left and right sides of the driving track by certain widths respectively to be used as a safety zone for unmanned mine card driving; converting the clustered elevated point cluster into two-dimensional data, calculating convex hull information of the two-dimensional data, calculating the distance between each convex hull point and a driving track, and judging whether the cluster is in a safety detection area of the unmanned mine card or not according to the distance value and the width of the safety area; if the distance value of one convex point is smaller than the width of the safety zone, the barrier is considered to be in the safety zone, otherwise, the barrier is not in the safety zone;
detecting whether the vehicle is in a drivable area;
matching the obstacle data acquired by the millimeter wave radar with the obstacle data acquired by the laser radar, and outputting a final result; the method specifically comprises the following steps: respectively judging whether the millimeter wave radar detection result is matched with the laser radar detection result according to the distance, dynamically distributing the distance threshold value through the size information of the obstacle detected by the laser radar, if the matching is successful, saving the laser radar detection result as a final detection result, and keeping the distance, size and orientation information of the obstacle; and if the matching fails, filtering the fault matching in the laser radar barriers, keeping the fault matching, and then outputting the effective barrier information of the fault matching.
2. The unmanned mine card obstacle detection method according to claim 1, wherein: the neighborhood in the neighborhood difference calculation is selected by considering grids within 2 meters around the grid as the neighborhood.
3. The unmanned mine card obstacle detection method according to claim 1, wherein: the method for separating the ground grid comprises the following steps: dividing all point clouds belonging to the ground into a plurality of point cloud intervals according to the distribution of the vehicle distances, performing plane fitting on the point cloud intervals, and connecting all fitted planes to obtain the road surface.
4. The unmanned mine card obstacle detection method of claim 1, wherein: and clustering the elevated points by using the multi-parameter model by adopting an eight-connected domain method to obtain elevated point cluster clusters, and merging a plurality of elevated point cluster clusters into one according to merging parameters to finish the target extraction of the point cloud.
5. The unmanned mine card obstacle detection method of claim 4, wherein: the merging parameters comprise the size and the density of the overhead point cluster.
6. The unmanned mine card obstacle detection method according to claim 1, wherein: the method for detecting whether the vehicle is in the drivable region comprises the following steps: selecting a certain point in front of the vehicle as a seed point, adopting a four-neighborhood growing mode, reserving a non-obstacle area in front of the mine card, calculating a distance value between a center point in front of the vehicle and the non-obstacle area, sequencing the distance value data to obtain a minimum value, comparing the minimum value with a set threshold value, if the minimum value is smaller than the set threshold value, adjusting the vehicle, otherwise, determining that the vehicle is safe.
7. The unmanned mine card obstacle detection method of claim 6, wherein: and selecting the center point in front of the vehicle as a seed point.
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| CN113139607B (en) * | 2021-04-27 | 2023-12-19 | 苏州挚途科技有限公司 | Obstacle detection method and device |
| CN115308769A (en) * | 2021-05-04 | 2022-11-08 | 南通智行未来车联网创新中心有限公司 | Method for identifying obstacle based on laser point cloud during automatic driving |
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| CN113567951B (en) * | 2021-09-26 | 2022-01-18 | 之江实验室 | Cross-millimeter wave radar target tracking method based on space-time information |
| CN116125488A (en) * | 2021-11-12 | 2023-05-16 | 北京万集科技股份有限公司 | Target tracking method, signal fusion method, device, terminal and storage medium |
| CN114387585B (en) * | 2022-03-22 | 2022-07-05 | 新石器慧通（北京）科技有限公司 | Obstacle detection method, detection device, and travel device |
| CN114994684B (en) * | 2022-06-01 | 2023-05-12 | 湖南大学无锡智能控制研究院 | Method and system for detecting obstacle in dust scene of multi-radar data fusion |
| CN117148837B (en) * | 2023-08-31 | 2024-08-23 | 上海木蚁机器人科技有限公司 | Dynamic obstacle determination method, device, equipment and medium |
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