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摘要
在激光雷达障碍物检测中，由于数据密度分布不均匀，传统DBSCAN聚类算法无法同时对近距离和远距离目标实现良好聚类，容易导致漏检和误检。为了解决这个问题，改进了传统DBSCAN算法聚类邻域半径ε参数的选值方法，不同于传统DBSCAN算法在聚类过程中使用统一的聚类邻域半径，而是调整为根据目标距离变化而变化的自适应聚类邻域半径。首先根据激光雷达扫描线分布求出相邻两条扫描线的间距建立ε*列表，然后依据每个扫描点的坐标值在列表中查找出对应的列表值，最后通过线性插值法确定对应的邻域半径。福特数据集的实验结果表明，优化之后的DBSCAN算法无论是对近距离目标还是远距离目标，其聚类效果均得到明显改善。与传统算法相比，障碍物检测正检率提高了17.52%。
Abstract
In the process of obstacle detection based on LiDAR, the traditional DBSCAN clustering algorithm can't achieve good clustering for both short-range and long-distance targets because of the uneven distribution of data density, resulting in missed detection or false detection. To solve the problem, this paper proposed an optimized DBSCAN algorithm which improves the adaptability under different distance by optimize the selection method of neighborhood radius. According to the distribution of the lines scanned by LiDAR, the distance between two adjacent scan lines is determined and an improved neighborhood radius list is established. Then the neighborhood radius will be searched in the list based on the coordinated values of each scan point. Finally, linear interpolation method is used to obtain the corresponding neighborhood radius. The experimental results based on Ford dataset prove that compared with the traditional DBSCAN algorithm, the proposed algorithm can effectively improve the accuracy of obstacle detection and adapt to the target clustering operation under different distances. The positive detection rate of obstacle detection is increased by 17.52%.
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Key words:
- obstacle detection /
- clustering /
- DBSCAN algorithm /
- neighborhood radius
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Overview
Overview: Obstacle detection is one of important research fields of intelligent vehicle environment perception technology. It is important for vehicles driving to detect the obstacles quickly and accurately. There are two main types of obstacle detection methods: based on visual sensors and based on LiDAR sensors. Since the latter method has the characteristics of no-susceptible to environmental impact, strong anti-interference, high ranging accuracy and etc, it is widely studied and applied in obstacle detection. Cluster analysis is one of the most commonly methods in LiDAR detection. Among them, DBSCAN algorithm is widely used because it can obtain clusters of arbitrary shape without knowing the number of classes in advance and can also identify noise points effectively. In order to detect obstacles quickly and accurately, this paper proposed an optimized DBSCAN algorithm which improves the adaptability under different distance by optimize the selection method of neighborhood radius. The procedure of obstacle detection in this paper includes four steps: road boundary detection, ROI region data extraction, ground data removal and optimized DBSCAN algorithm clustering. Firstly, use the characteristic that the structured road boundary point has obvious elevation mutation than the ground point, detect the local Z-value abrupt changing point and use the least square method to fit out the road boundary. Then, according to road boundary, extract the data of the inside area (the ROI area) of the road boundary. Next, fit the ground plane in ROI area and remove them from ROI. Finally, use optimized DBSCAN algorithm to handle the data in ROI after boundary detecting and removing. The Ford Campus dataset which is acquired by the University of Michigan and Ford Motor Company is used to test the performance of the optimized DBSCAN algorithm. The experiments were performed on a computer with 4 GB memory and 3 GHz clock frequency, and programmed on MATLAB. The experiment results show that the effect of the optimized DBSCAN algorithm is significantly improved for both short-range and long-range targets. Compared with the traditional DBSCAN algorithm, the positive detection rate of obstacle detection improves and the false detection rate reduced significantly. Since some false detections caused by road boundary detection error, we can improve the accuracy of boundary detection by multi-sensors fusion in the future. Considering the driving environment of the unmanned vehicle, multi-sensors fusion can be applied to the algorithm, and the robustness and stability of the algorithm will be further improved.
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图 6 双距离障碍物检测实验。(a)双距离障碍物数据采集场景；(b)激光点云图；(c)传统DBSCAN算法聚类效果；(d)优化DBSCAN算法聚类效果
Figure 6. The experiment of double-distance obstacle detection. (a) Dataset scene of two distance obstacle; (b) LiDAR points cloud; (c) Traditional DBSCAN algorithm; (d) Optimized DBSCAN algorithm
图 7 三距离障碍物检测实验。(a)数据采集场景；(b)传统DBSCAN聚类算法效果；(c)优化DBSCAN聚类效果
Figure 7. The experiment of three-distance obstacle detection. (a) Dataset scene; (b) Traditional DBSCAN algorithm; (c) Optimized DBSCAN algorithm
图 8 多种距离障碍物检测实验。(a)数据采集场景；(b)传统DBSCAN聚类算法效果；(c)优化DBSCAN聚类效果
Figure 8. The experiment of multi-distance obstacle detection. (a) Dataset scene; (b) Traditional DBSCAN algorithm; (c) Optimized DBSCAN algorithm
表 1 传统DBSCAN算法和优化DBSCAN算法性能对比
Table 1. Performance comparison of traditional and optimized DBSCAN algorithm
连续帧数 300 500 800 检测结果 正检 误检 漏检 正检 误检 漏检 正检 误检 漏检 传统DBSCAN算法 209 27 87 346 41 139 539 69 253 优化DBSCAN算法 253 42 13 438 59 21 695 97 41 
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