Real-time 3D-Lidar, MMW Radar and GPS/IMU fusion based vehicle detection and tracking in unstructured environment

计算机科学 惯性测量装置 全球定位系统 计算机视觉 激光雷达 稳健性(进化) 人工智能 传感器融合 雷达 雷达跟踪器 车辆跟踪系统 跟踪系统 实时计算 遥感 卡尔曼滤波器 地理 电信 基因 生物化学 化学
作者
Ning Li,Caixia Lu,Xuewei Yu,Xueyan Liu,Bo Su
标识
DOI:10.1109/icra48506.2021.9562063
摘要

To solve the problem of unmanned ground vehicle leader-follower formation transportation in unstructured environment, we propose a novel target detection and tracking method based on multi-sensor fusion perception. Combined with 3D-Lidar, millimeter wave Radar and GPS/IMU, the proposed method can achieve stable target detection and continuous tracking of both static and dynamic vehicles. First, 3D-Lidar is used to detect the geometric model of the leader vehicle to complete the initialization of tracking target and it can also be assisted for target tracking. Then during the movement, the dynamic leader is mainly tracked through millimeter wave Radar as this sensor can keep tracking the same target with a constant index and effectively distinguish dynamic vehicle from other static obstacles according to relative speed estimation. In addition, by using GPS/IMU based integrated navigation, the movement trend of the leader can be derived according to the echo vehicle pose information and the relative position relationship. This is helpful to reduce the region of interest for target tracking and improve the real-time performance. In different unstructured environments, we perform the leader-follower formation transportation experiments for hundreds of kilometers. In rough terrain, the maximum tracking speed can still reach 40km/h and the maximum tracking distance can be up to 100 meters. Experiments show that the proposed method is suitable for vehicle target detection and tracking in unstructured environment. It has good robustness and high real-time performance with an average processing frame rate of 20Hz. The proposed method can be used for the formation transportation of unmanned ground vehicles to reduce labor costs.
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