Motion capture through tracking retroreflectors obtains highly accurate pose estimation, which is frequently used in robotics. Unlike commercial motion capture systems, fiducial marker-based tracking methods, such as AprilTags, can perform relative localization without requiring a static camera setup. However, popular pose estimation methods based on fiducial markers have lower localization accuracy than commercial motion capture systems. We propose Mobile MoCap, a system that utilizes inexpensive near-infrared cameras for accurate relative localization even while in motion. 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 the proposed localization technique while in motion, we mount our Mobile MoCap system, as well as an RGB camera to benchmark against fiducial markers, onto a precision-controlled linear rail and servo. The fiducial marker approach employs AprilTags, which are pervasively used for localization in robotics. We evaluate the two systems at varying distances, marker viewing angles, and relative velocities. Across all experimental conditions, our stereo-based Mobile MoCap system 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：通过跟踪回射器进行动作捕捉可获得高精度的姿态估计，该方法在机器人领域应用广泛。与商用动作捕捉系统不同，基于基准标记的跟踪方法（如AprilTags）无需固定摄像机设置即可实现相对定位。然而，当前基于基准标记的主流姿态估计方法的定位精度低于商用动作捕捉系统。本文提出移动式动作捕捉系统（Mobile MoCap），该系统利用廉价近红外摄像机实现高精度相对定位，即使在运动状态下仍能保持性能。我们设计了一种回射器特征检测器，可执行六自由度（6-DoF）跟踪，并通过最小化相机曝光时间减少运动模糊。为评估所提方法在运动场景下的定位性能，我们将Mobile MoCap系统与RGB摄像机（用于对比基准标记）安装于精密控制的直线导轨与伺服系统上。基准标记方法采用在机器人定位中广泛使用的AprilTags系统。我们在不同距离、标记视角及相对速度条件下对两种系统进行评估。实验结果显示，在所有测试条件下，基于立体视觉的Mobile MoCap系统在位置与姿态精度上均优于基准标记法。Mobile MoCap代码基于ROS 2实现，并已开源至https://github.com/RIVeR-Lab/mobile_mocap。