在具有挑战性的室内场景中,用人类运动完成点云
Chengsi Zhang1, Stephen Czarnuch2
1Department of Electrical and Computer Engineering, Faculty of Engineering and Applied Science, Memorial University of Newfoundland, St. John's, NL, Canada.
Frontiers in robotics and AI
|May 30, 2023
概括
本研究提出了一种新的方法,用于从不同视角的多个传感器记录3D点云数据. 这种方法精确地对准了人类的行走路径,使得在复杂的环境中能够精确地估计传感器转换矩阵.
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数据处理 3D数据处理
背景情况:
- 结合来自不同视角的多个传感器的点云数据是具有挑战性的.
- 动态,混乱的环境和有限的场景重叠使传感器融合复杂化.
研究的目的:
- 开发一种新的方法来记录来自两个传感器的3D点云数据,其相对视角未知.
- 为了在现实生活中的场景中实现精确的传感器融合,与人类运动.
主要方法:
- 通过对齐地面平面来减少3D点云完成未知.
- 使用基于直方图的方法提取3D人类行走序列.
- 通过最小化Fréchet距离来匹配步行路径,并使用2D代最近点 (ICP) 进行最终对齐.
主要成果:
- 在两个摄像头截图之间成功记录了人类步行路径.
- 估计了两个传感器之间的转换矩阵.
- 在具有挑战性的环境中展示了一种强大的传感器注册方法.
结论:
- 提出的方法有效地处理了显著的视角差异和复杂的场景.
- 为动态环境提供准确的传感器注册和数据融合.
- 便利需要多传感器3D数据集成的现实应用.
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