3D Object Detection Based on Extremely Sparse Laser Point Cloud and RGB Images

The task of detecting 3D objects in complex traffic scenes is crucial and challenging. To address the high-cost problem of high-definition LiDAR and the poor effect of detection algorithms based on the millimeter wave radar and cameras used in mainstream detection algorithms, this study proposes a 3D target detection algorithm using low-definition LiDAR and a camera, which can significantly reduce the hardware cost of autonomous driving. To obtain a depth map, the 64-line LiDAR point cloud is first downsampled to 10% of the original point clouds, resulting in an extremely sparse point cloud, and fed to the depth-completion network with RGB images. Then, a point cloud bird-eye view is generated from the depth map based on the proposed algorithm for calculating the point cloud intensity. Finally, the point cloud bird-eye view is fed into the detection network to obtain the geometric information, heading angle, and category of the target stereo hounding box. The different algorithms are experimentally validated using KITT1 dataset. The experimental results demonstrate that the proposed algorithm can outperform some conventional high-definition LiDAR-based detection algorithms in terms of detection accuracy.