A common prerequisite for evaluating a visual(-inertial) odometry (VO/VIO) algorithm is to align the timestamps and the reference frame of its estimated trajectory with a reference ground-truth derived from a system of superior precision, such as a motion capture system. The trajectory-based alignment, typically modeled as a classic hand-eye calibration, significantly influences the accuracy of evaluation metrics. However, traditional calibration methods are susceptible to the quality of the input poses. Few studies have taken this into account when evaluating VO/VIO trajectories that usually suffer from noise and drift. To fill this gap, we propose a novel spatiotemporal hand-eye calibration algorithm that fully leverages multiple constraints from screw theory for enhanced accuracy and robustness. Experimental results show that our algorithm has better performance and is less noise-prone than state-of-the-art methods.

翻译：在评估视觉（惯性）里程计（VO/VIO）算法时，一个常见的前提条件是将算法估计轨迹的时间戳和参考坐标系与由更高精度系统（如运动捕捉系统）生成的参考真实轨迹对齐。这种基于轨迹的对齐通常建模为经典的手眼标定问题，其对评估指标的准确性有显著影响。然而，传统标定方法易受输入位姿质量的影响。在评估通常存在噪声和漂移的VO/VIO轨迹时，鲜有研究考虑这一因素。为弥补这一不足，我们提出了一种新颖的时空手眼标定算法，该算法充分利用螺旋运动理论中的多重约束，以提升精度和鲁棒性。实验结果表明，与现有最先进方法相比，我们的算法性能更优且对噪声更不敏感。