基于自适应多特征融合的路沿检测与跟踪方法研究

Research on Curb Detection and Tracking Method Based on Adaptive Multi-feature Fusion

为减少路沿检测过程中存在的误检和漏检,以三维激光雷达为传感器,提出了一种新的路沿检测与跟踪方法。首先,对点云进行预处理,采用基于距离的滤波器对原始点云中存在的影响特征提取的干扰点进行滤除,以提高路沿点的提取精度,对滤波后的点云,采用地面平面分段拟合的地面分割方法提取地面点云;然后,利用高度差、平滑度与角度阈值等路沿空间特征,设计了一种自适应多特征融合的路沿点提取算法;其次,针对由障碍物遮挡所造成的部分路沿缺失问题,利用饶-布莱克维尔化粒子滤波跟踪器对路沿点进行跟踪预测;最后,将该方法应用于无人环卫车进行了多工况实验,结果表明该方法能准确地检测出道路的边界信息,有效地减少了路沿点的误检和漏检。

For reducing the false detection and miss detection in the process of curb detection,a novel curb detection and tracking method is proposed with 3D-LIDAR as sensor.Firstly,the point cloud is preprocessed,and a distance-based filter is used to filter the interference points in the original point cloud,that affect feature extraction,are filtered by a distance-based filter to enhance the extraction accuracy of curb points.For the filtered point cloud,the ground segmentation method with ground plane segment-wise fitting is used to extract the ground point cloud.Then,an adaptive multi-feature fusion algorithm for curb point extraction is designed by using the spatial features of curbs i.e.height difference,smoothness and angle threshold.Next,aiming at the problem of partial curb loss caused by obstacles,the Rao-Blackwellized particle filter tracker is used to track and predict the curb points.Finally,the method is applied to the multi-condition experiments of the unmanned sanitation vehicle,and the results show that the method can accurately detect the road boundary information,and effectively reduce the false detection and missing detection of curb points.