A study on 3D LiDAR-based point cloud object detection using an enhanced PointPillars network

Abstract The PointPillar target detection algorithm is a mainstream 3D lidar point cloud target detection algorithm that has a fast response speed but low detection accuracy. Addressing the problem of the low detection accuracy of the PointPillar target detection network, we propose an improved PointPillar target detection algorithm that integrates an attention mechanism. The algorithm first introduces the attention mechanism and strengthens the feature extraction module based on PointPillar to realize the amplification of the local information in the three scale feature maps and to better extract the more important feature information. Then, our algorithm adds an anchor free type detector head to further optimize the detector head module. The experimental results show that the optimized PointPillar target detection algorithm has achieved good test results in the KITTI data set. Under medium difficulty, the AOS mode mAP reaches 79.76%, the 3D mode mAP reaches 82.03%, and the BEV mode mAP reaches 82.30%. Compared with that of other point cloud target detection algorithms, the detection accuracy of our algorithm is improved by approximately 10%.