Point Cloud Compression for 3D LiDAR Sensor using Recurrent Neural Network with Residual Blocks

Abstract

The use of 3D LiDAR, which has proven its capabilities in autonomous driving systems, is now expanding into many other fields. The sharing and transmission of point cloud data from 3D LiDAR sensors has broad application prospects in robotics. However, due to the sparseness and disorderly nature of this data, it is difficult to compress it directly into a very low volume. A potential solution is utilizing raw LiDAR data. We can rearrange the raw data from each frame losslessly in a 2D matrix, making the data compact and orderly. Due to the special structure of 3D LiDAR data, the texture of the 2D matrix is irregular, in contrast to 2D matrices of camera images. In order to compress this raw, 2D formatted LiDAR data efficiently, in this paper we propose a method which uses a recurrent neural network and residual blocks to progressively compress one frame's information from 3D LiDAR. Compared to our previous image compression based method and generic octree point cloud compression method, the proposed approach needs much less volume while giving the same decompression accuracy. Potential application scenarios for point cloud compression are also considered in this paper. We describe how decompressed point cloud data can be used with SLAM (simultaneous localization and mapping) as well as for localization using a given map, illustrating potential uses of the proposed method in real robotics applications.