Continuous point cloud data compression using SLAM based prediction

Interest in continuous point cloud data has increased rapidly as it becomes more widely used in practical applications, such as the development of autonomous driving systems. Sharing and storing continuous point cloud data is currently expensive and difficult due to the huge volume of data involved, however. As a result, developing efficient methods of compressing cloud point data has become an urgent task. Current methods compress the data directly using tree structures or height map-based methods. Taking into consideration the specialized requirements of autonomous driving, in [1] we proposed a method of compressing raw point cloud data using image compression methods and obtained superior results. Image compression-based methods are not able to utilize the 3D characteristics of point clouds, however. Therefore, in this paper we propose a new compression method using location and orientation information from Simultaneous Localization and Mapping (SLAM). Proposed method can take advantage of the 3D characteristic of point cloud by a predicting process which simulating the working procedure of 3D LiDAR. Strategies from MPEG and DPCM compression are utilized and several issues which can affect performance are discussed. In addition, based on proposed method, this paper discusses the ways to segment steam point cloud for compression task. We call this segmentation process adaptive sequence decision. We then compare the proposed SLAM-based method with image compression-based methods in various situations. Our experimental results show that, while requiring a small margin of error, the SLAM-based method outperforms image compression-based methods.