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摘要:
在园区环境中，无人车装载单一毫米波雷达或激光雷达传感器进行障碍物检测跟踪时存在探测范围有限、准确率低及稳定性差等问题。为此，提出一种基于毫米波雷达与激光雷达融合的多障碍物检测跟踪方法。利用改进欧氏聚类算法对道路内激光点云目标进行提取，基于信息筛选策略获得毫米波雷达数据中的有效目标；基于目标检测交并比与可靠性分析，对2种目标进行自适应融合，并利用跟踪门与联合概率数据关联(JPDA)算法完成前后帧数据匹配；应用多运动模型交互与无迹卡尔曼滤波实现障碍物跟踪。实车实验表明：相比单一毫米波雷达与激光雷达障碍物检测跟踪，所提方法有更好的准确性与稳定性。
Abstract:Limited detecting range, low precision, and poor stability are just a few of the issues with obstacle detection and tracking that arise when using a single millimeter wave radar, or LiDAR, on an unmanned vehicle in a park. An obstacle-detecting and tracking approach based on the fusion of radar and LiDAR is proposed. Firstly, the improved Euclidean clustering algorithm is adopted to extract the objects in the road boundary from LiDAR point clouds. Furthermore, effective objects can be obtained from millimeter wave radar data which is handled based on an information filtering strategy. Then, the adaptive fusion of two kinds of objects described above is carried out based on the intersection over union and reliability analysis of objectdetection. The tracking gate and the joint probabilistic data association (JPDA) algorithm are performed to match sequence frames. In order to achieve obstacle tracking, the interacting multiple model and unscented Kalman filter method are finally put into practice. The experimental results show that the proposed method has higher accuracy and stability than using a single sensor for obstacle detection and tracking.
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Key words:
- millimeter wave radar /
- LiDAR /
- unmanned vehicle /
- sensor fusion /
- obstacle tracking
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表 1 不同传感器跟踪结果对比
Table 1. Comparison of tracking results of different sensors
处理方法 $ {N_{{\text{MOTA}}}} $/% $ {N_{{\text{IDS}}}} $ $ {N_{{\text{FRAG}}}} $ 激光雷达 86.69 14 20 毫米波雷达 74.03 21 36 融合策略 93.58 11 19 
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