基于稀疏彩色点云的自动驾驶汽车3D目标检测方法

3D Object Detection Method for Autonomous Vehicle Based on Sparse Color Point Cloud

针对目前在自动驾驶汽车中,目标检测的点云分割与识别算法的准确率低等问题,提出一种稀疏彩色点云结构,该结构由摄像头采集的图像信息与激光雷达采集的点云信息进行空间匹配与特征叠加后生成。通过改进的PointPillars神经网络算法对融合后的彩色稀疏点云进行运算。实验结果表明,本方法在平均精度上比原算法有较大的提升,尤其是对行人和骑单车人的识别平均精度的提升更为明显,在中等难度下的行人和骑单车人3D检测的平均精度值分别提升13.8%和6.6%,显示了本方法的有效性。

Aiming at the current problem of the low accuracy of point cloud segmentation and recognition algorithm in object detection of autonomous vehicle,a sparse color point cloud(SCPC)structure is proposed,which is formed by spatial matching and feature superposition of the image information collected by camera and the point cloud information acquired from lidar.Then,the improved PointPillars neural network algorithm is adopted to conduct operation on the fused SCPC.The results of experiment show that this method can achieve a major rise in average accuracy,compared with original PointPillars algorithm,especially the recognition accuracy of pedestrians and cyclists.The average accuracy of pedestrian and cyclist detections on 3D view under medium difficulty increases by 13.8%and 6.6%respectively,demonstrating the effectiveness of the method adopted.