Motion capture (MoCap) through tracking retroreflectors obtains high precision pose estimation, which is frequently used in robotics. Unlike MoCap, fiducial marker-based tracking methods do not require a static camera setup to perform relative localization. Popular pose-estimating systems based on fiducial markers have lower localization accuracy than MoCap. As a solution, we propose Mobile MoCap, a system that employs inexpensive near-infrared cameras for precise relative localization in dynamic environments. We present a retroreflector feature detector that performs 6-DoF (six degrees-of-freedom) tracking and operates with minimal camera exposure times to reduce motion blur. To evaluate different localization techniques in a mobile robot setup, we mount our Mobile MoCap system, as well as a standard RGB camera, onto a precision-controlled linear rail for the purposes of retroreflective and fiducial marker tracking, respectively. We benchmark the two systems against each other, varying distance, marker viewing angle, and relative velocities. Our stereo-based Mobile MoCap approach obtains higher position and orientation accuracy than the fiducial approach. The code for Mobile MoCap is implemented in ROS 2 and made publicly available at https://github.com/RIVeR-Lab/mobile_mocap.

翻译：通过追踪反向反射器进行动作捕捉（MoCap）可获得高精度的位姿估计，该方法在机器人领域应用广泛。与MoCap不同，基于基准标记的追踪方法无需固定摄像机设置即可实现相对定位。然而，基于基准标记的常见位姿估计系统其定位精度低于MoCap系统。为此，我们提出Mobile MoCap系统，该系统采用廉价近红外摄像机在动态环境中实现高精度相对定位。我们设计了一种可执行六自由度追踪的反向反射器特征检测器，该检测器通过极短的摄像机曝光时间有效减少运动模糊。为评估不同定位技术在移动机器人场景中的表现，我们将Mobile MoCap系统与标准RGB摄像机分别安装在精密控制线性导轨上，用于反向反射器追踪与基准标记追踪。通过改变距离、标记视角及相对速度，对两种系统进行对比测试。基于立体视觉的Mobile MoCap方法在位置与姿态精度方面均优于基准标记方法。Mobile MoCap的代码已基于ROS 2实现，并公开发布于https://github.com/RIVeR-Lab/mobile_mocap。