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一个基于点云压缩和边界提取的新型快速行人识别算法
1Zhong Shan Polytechnic, Zhongshan, China.
PeerJ. Computer science
|June 22, 2023
概括
这项研究引入了一种压缩激光雷达点云数据的新方法,大大提高了自动驾驶系统中行人识别精度. 该方法增强了数据压缩,同时保留了关键功能,以实现更快,更可靠的检测.
科学领域:
- 计算机视觉 计算机视觉
- 机器人技术 机器人技术 机器人技术
- 传感器数据处理 传感器数据处理
背景情况:
- 自动驾驶的安全依赖于准确的行人识别.
- 激光雷达提供高分辨率的3D点云数据,但面临着巨大的数据大小和冗余性的挑战.
- 对点云数据的高效处理对于实时应用,如路径规划和障碍回避至关重要.
研究的目的:
- 开发一个快速的行人识别算法,使用合激光雷达点云数据.
- 提出有效的点云数据压缩方法,以应对传输,存储和处理速度方面的挑战.
- 提高自动驾驶系统中行人检测的准确性和效率.
主要方法:
- 利用激光雷达的融合点云数据用于行人识别.
- 开发了基于特征点提取和减少voxel网格的点云数据压缩技术.
- 在压缩点云数据上使用基于图像映射的算法进行行人识别.
主要成果:
- 拟议的压缩算法提高了6.02%的峰值信号噪声比.
- 识别准确度有显著的改善:16.93% (简单的场景),17.2% (中等场景) 和16.12% (困难的场景).
- 在精度和特征保留方面表现优于随机抽样压缩方法.
结论:
- 拟议的方法实现了激光雷达点云的优越数据压缩.
- 在压缩数据中有效保留关键特征点,确保识别质量.
- 压缩的点云数据使自动驾驶应用程序更快,更准确地识别行人.
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