StOCaMo: Online Calibration Monitoring for Stereo Cameras

Moravec, J., Šára, R. (2023). StOCaMo: Online Calibration Monitoring for Stereo Cameras. In: Pertusa, A., Gallego, A.J., Sánchez, J.A., Domingues, I. (eds) Pattern Recognition and Image Analysis. IbPRIA 2023. Lecture Notes in Computer Science, vol 14062. Springer, Cham.

Cameras are the prevalent sensors used for perception in autonomous robotic systems, but initial calibration may degrade over time due to dynamic factors. This may lead to the failure of the downstream tasks, such as simultaneous localization and mapping (SLAM) or object recognition. Hence, a computationally light process that detects the decalibration is of interest.

We propose StOCaMo, an online calibration monitoring procedure for a stereoscopic system. StOCaMo is based on epipolar constraints; it validates calibration parameters on a single frame with no temporal tracking. The main contribution is the use of robust kernel correlation, which is shown to be more effective than the standard epipolar error.

StOCaMo was tested on two real-world datasets: EuRoC MAV and KITTI. With fixed parameters learned on a realistic synthetic dataset from CARLA, it achieved 96.2% accuracy in decalibration detection on EuRoC and KITTI. In the downstream task of detecting SLAM failure, StOCaMo achieved 87.3% accuracy, and its output has a rank correlation of 0.77 with the SLAM error. These results outperform a recent method by Zhong et al., 2021. (© Springer)