室内LiDAR点云的两步过方法:有效地去除跳跃点和错误检测点
Yibo Cao1, Yonghao Huang1, Junheng Ni1
1School of Artificial intelligence, South China Normal University, Foshan 528000, China.
Sensors (Basel, Switzerland)
|October 16, 2025
概括
本研究介绍了一种两步过方法,以改善室内移动机器人的点云数据. 该技术有效地减少了反射表面和物体边缘的误差,提高了机器人导航的准确性和稳定性.
科学领域:
- 机器人技术 机器人技术 机器人技术
- 计算机视觉 计算机视觉
- 传感器数据处理 传感器数据处理
背景情况:
- 准确的点云数据对于室内移动机器人定位和映射 (SLAM) 是必不可少的.
- 挑战包括由于传感器错误或反射性导致的物体边缘和光滑表面的错误检测点 (跳跃点).
- 这些错误会降低机器人的感知和导航性能.
研究的目的:
- 开发和验证一个强大的两步过方法,以增强室内移动机器人点云数据.
- 解决在光滑表面上跳跃点和错误检测点的具体问题.
- 为了提高机器人定位和映射的整体准确性和稳定性.
主要方法:
- 实施了两步过方法.
- 步骤1:根据辐射距离和触点跨度进行聚类过,以删除跳跃点.
- 步骤2:网格透模型过以消除在光滑平面上错误检测到的点.
主要成果:
- 拟议的两步过方法显著减少了室内环境中的跳跃点和错误检测点.
- 实验结果显示,点云质量明显改善.
- 过方法提高了室内移动机器人的导航精度和稳定性.
结论:
- 两步过方法在清理室内移动机器人的杂点云数据方面是有效的.
- 这种方法提高了SLAM系统在具有挑战性的室内环境中的可靠性.
- 提高点云质量导致机器人导航更强大,更稳定.
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