Abstract
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.
Abstract (translated)
评估视觉惯性导航算法（VO/VIO）的常见先决条件是将其估计轨迹的时标和参考帧与从高级精度系统（如运动捕捉系统）生成的参考地面参考系对齐。基于轨迹的对齐通常建模为经典的手眼校准，显著影响了评估指标的准确性。然而，传统的校准方法容易受到输入姿态质量的影响。在评估通常存在噪声和漂移的VO/VIO轨迹时，很少有研究考虑这一点。为了填补这一空白，我们提出了一个新颖的spatiotemporal hand-eye校准算法，它完全利用螺纹理论的多个约束以提高准确性和稳健性。实验结果表明，我们的算法具有更好的性能，并且比最先进的 methods噪声更小。
URL
https://arxiv.org/abs/2404.14894
