Joint Spatial-Temporal Calibration for Camera and Global Pose Sensor
Junlin Song Antoine Richard Miguel Olivares-Mendez
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.