Wang Qiang, Jiang Liuyang, Sun Xuebin, Zhao Jingbo, Deng Zhaopeng, Yang Shizhong
College of Information and Control Engineering, Qingdao University of Technology, Qingdao 266525, China.
State Key Laboratory of Precision Measuring Technology and Instruments, Tianjin University, Tianjin 300072, China.
Sensors (Basel). 2022 Jul 7;22(14):5108. doi: 10.3390/s22145108.
In this article, we present an efficient coding scheme for LiDAR point cloud maps. As a point cloud map consists of numerous single scans spliced together, by recording the time stamp and quaternion matrix of each scan during map building, we cast the point cloud map compression into the point cloud sequence compression problem. The coding architecture includes two techniques: intra-coding and inter-coding. For intra-frames, a segmentation-based intra-prediction technique is developed. For inter-frames, an interpolation-based inter-frame coding network is explored to remove temporal redundancy by generating virtual point clouds based on the decoded frames. We only need to code the difference between the original LiDAR data and the intra/inter-predicted point cloud data. The point cloud map can be reconstructed according to the decoded point cloud sequence and quaternion matrices. Experiments on the KITTI dataset show that the proposed coding scheme can largely eliminate the temporal and spatial redundancies. The point cloud map can be encoded to 1/24 of its original size with 2 mm-level precision. Our algorithm also obtains better coding performance compared with the octree and Google Draco algorithms.
在本文中，我们提出了一种用于激光雷达点云地图的高效编码方案。由于点云地图由大量拼接在一起的单次扫描组成，通过在地图构建过程中记录每次扫描的时间戳和四元数矩阵，我们将点云地图压缩转化为点云序列压缩问题。编码架构包括两种技术：帧内编码和帧间编码。对于帧内，开发了一种基于分割的帧内预测技术。对于帧间，探索了一种基于插值的帧间编码网络，通过基于解码帧生成虚拟点云来消除时间冗余。我们只需要对原始激光雷达数据与帧内/帧间预测的点云数据之间的差异进行编码。可以根据解码后的点云序列和四元数矩阵重建点云地图。在KITTI数据集上的实验表明，所提出的编码方案可以在很大程度上消除时间和空间冗余。点云地图可以以2毫米级的精度编码到其原始大小的1/24。与八叉树和谷歌Draco算法相比，我们的算法也获得了更好的编码性能。
