A Real-time Compression Algorithm of Color Point Cloud Streams for Environmental Scanning

The problems of compressing and encoding the large⁃scale and high⁃dimensional point cloud data to improve transmission efficiency arise with the continuous development of digital twin technology. However, most point cloud coding methods have weak real⁃time compression, low compression efficiency, and high requirement of point cloud format. A real⁃time color stream Draco (RCS⁃Draco) compression algorithm based on Google Draco geometric compression library is proposed to solve these problems. By integrating the algorithm into the ROS framework, the point cloud stream is encoded and decoded in real time by means of ROS message flow, which improves the real⁃time performance of the algorithm. An optimal clipping model is established to clip and filter the point cloud, and remove the drift and outlier point cloud, thus improving the compression efficiency of the algorithm. The RGB color information of point cloud is encoded by establishing a quantitative prediction model, which solves the problem that most point cloud compression algorithms cannot process color information. By adjusting the compression grade and quantization parameters, it is proved that the average compression rate of RCS⁃Draco algorithm can reach 77%, the average compression and decompression time is less than 0. 035 s, the average position error is less than 0. 05 m, and the average attribute error is less than 35. The RCS⁃Draco algorithm is superior to Draco algorithm in every index through the integration test. The experimental results show that the RCS⁃Draco compression algorithm performs well in terms of real⁃time compression, efficiency and point cloud format, and can effectively improve transmission efficiency. © 2023 China Ordnance Industry Corporation. All rights reserved.