In robotics, motion capture systems have been widely used to measure the accuracy of localization algorithms. Moreover, this infrastructure can also be used for other computer vision tasks, such as the evaluation of Visual (-Inertial) SLAM dynamic initialization, multi-object tracking, or automatic annotation. Yet, to work optimally, these functionalities require having accurate and reliable spatial-temporal calibration parameters between the camera and the global pose sensor. In this study, we provide two novel solutions to estimate these calibration parameters. Firstly, we design an offline target-based method with high accuracy and consistency. Spatial-temporal parameters, camera intrinsic, and trajectory are optimized simultaneously. Then, we propose an online target-less method, eliminating the need for a calibration target and enabling the estimation of time-varying spatial-temporal parameters. Additionally, we perform detailed observability analysis for the target-less method. Our theoretical findings regarding observability are validated by simulation experiments and provide explainable guidelines for calibration. Finally, the accuracy and consistency of two proposed methods are evaluated with hand-held real-world datasets where traditional hand-eye calibration method do not work.

翻译：在机器人领域，运动捕捉系统已被广泛用于评估定位算法的精度。此外，该基础设施还可用于其他计算机视觉任务，例如视觉（-惯性）SLAM动态初始化的评估、多目标跟踪或自动标注。然而，为了达到最优性能，这些功能需要相机与全局位姿传感器之间具备精确可靠的时空标定参数。本研究提出了两种新颖的解决方案来估计这些标定参数。首先，我们设计了一种基于标定物的离线方法，具有高精度和高一致性，可同时优化时空参数、相机内参和轨迹。随后，我们提出了一种在线无标定物方法，无需标定目标即可估计时变时空参数。此外，我们针对无标定物方法进行了详细的能观性分析，并通过仿真实验验证了关于能观性的理论发现，为标定提供了可解释的指导性准则。最后，我们使用手持真实世界数据集对所提两种方法的精度和一致性进行了评估，而传统手眼标定方法在此类数据集上无法生效。