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兵工学报 ›› 2023, Vol. 44 ›› Issue (S2): 167-177.doi: 10.12382/bgxb.2023.0856

所属专题: 群体协同与自主技术

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一种面向环境扫描的彩色点云流实时压缩算法

马景起1, 于脐文1, 黄平1,*(), 王伟1, 李友为2   

  1. 1 哈尔滨工程大学 智能科学与工程学院, 黑龙江 哈尔滨 150009
    2 哈尔滨第一机械集团有限公司, 黑龙江 哈尔滨 150000
  • 收稿日期:2023-09-04 上线日期:2024-01-10
  • 通讯作者:
  • 基金资助:
    国家自然科学基金项目(62271163)

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

MA Jingqi1, YU Qiwen1, HUANG Ping1,*(), WANG Wei1, LI Youwei2   

  1. 1 School of Intelligent Science and Engineering, Harbin Engineering University, Harbin 150009, Heilongjiang, China
    2 Harbin First Machinery Group Corporation, Harbin 150000, Heilongjiang, China
  • Received:2023-09-04 Online:2024-01-10

摘要:

随着数字孪生技术的不断发展,人们面临将大规模、高维度的点云数据进行压缩编码的问题,以提高传输效率。然而大多点云编码方式存在压缩实时性不强、压缩效率低、点云格式要求过高等问题。针对解决这些问题,提出了一种基于谷歌Draco几何压缩库的实时彩色点云流压缩(Real-time Color Stream Draco,RCS-Draco)算法。将算法集成到ROS框架内,借助ROS消息流,对点云流实时地进行编码和解码,提高了算法的实时性;通过建立优化裁剪模型,对点云进行裁剪和滤波,去除了漂移和离群点云,提高了压缩算法的压缩效率。建立量化预测模型,对点云的RGB颜色信息进行编码,解决了大多数点云压缩算法无法处理颜色信息的问题。对比试验通过调整压缩等级和量化参数,证明RCS-Draco算法的平均压缩率最高能够达到77%、平均压缩和解压时间小于0.035s、位置平均误差小于0.05m、属性平均误差小于35;并通过相融试验证明RCS-Draco算法在各项指标上优于Draco算法。试验结果表明,RCS-Draco压缩算法在压缩实时性、效率、点云格式方面均表现良好,能够有效地提升传输效率。

关键词: 数字孪生, 压缩编码, 彩色点云流, ROS框架

Abstract:

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 0.035s, the average position error is less than 0.05m, 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.

Key words: digital twins, compression coding, color point cloud stream, ROS framework

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