集群式点云注册:基于集群式的灵活采样注册方法,用于低重叠点云.
Xinrui Huang1, Xiaorong Gao1, Jinlong Li1
1School of Physical Science and Technology, Southwest Jiaotong University, Chengdu 610031, China.
Sensors (Basel, Switzerland)
|September 14, 2024
概括
这项研究引入了CL-PCR,一种新的3D点云注册方法. 它提高了准确性和稳定性,特别是低重叠和异常值,优于现有技术.
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
- 3D 感知 3D 感知
背景情况:
- 3D点云注册对于基于传感器的3D感知至关重要.
- 像RANSAC这样的传统方法在低重叠,异常值和计算成本方面扎.
研究的目的:
- 开发一种新的3D注册方法,CL-PCR,以克服现有技术的局限性.
- 提高对点云注册中低重叠和异常值的稳定性.
主要方法:
- 引入了CL-PCR,这是一种基于最大集群和SC^2-PCR框架的新方法.
- 为通信兼容性构建了一个图形矩阵,并使用类似集团的子集来达成共识.
- 采用SVD进行转换假设计算,并根据评估指标选择最好的.
主要成果:
- 在3DMatch/3DLoMatch数据集上,CL-PCR表现出卓越的注册性能,特别是快速CL-PCRv1.
- 该方法显示了对低重叠和减少异常影响的增强稳定性.
- 通过真实数据验证有效性和实用性.
结论:
- CL-PCR为3D点云注册提供了强大而准确的解决方案.
- 该方法利用较小的采样子集的能力,在具有挑战性的环境中提高了性能.
- 在传感器的3D感知中,CL-PCR代表了显著的进步.
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