基于改进PointPillars的自动驾驶障碍物点云检测算法OA

To solve the problems of high false detection rate of interferential point clouds and high missing detection rate of distant sparse point clouds in automatic driving scene,the obstacle point cloud detection algorithm based on improved PointPillars was proposed.The point cloud in pillars was encoded by the aggregation module and shared multi-layer perceptron(MLP).The salient and detailed features were mapped into pseudo-image features by stacking the max-pooling and average-pooling.To solve the problem of pseudo-image feature with insufficient attention and utilization,the deep and shallow feature maps were fused by attention and residual second block(ARSB)module to optimize the gradient and enhance the coordinate attention(CA)to effective targets.The results show that the improved algorithm has high detection accuracy for global point clouds.The detection precision of the improved algorithm is better than those of the classical 3D detection algorithms of PointPillars and STD methods,especially for the detection of car category.The detection speed is fast,which meets the requirements of real-time.