Correcting decalibration of stereo cameras in self-driving vehicles

• DOI: 10.48550/arxiv.2001.05267
• Publication Date: 2020-06-07
• Authors: Jon Muhovič; Janez Perš
• Keywords: FOS: Computer and information sciences; meritve razdalje; I.4; samovozeča vozila; Chemical technology; Computer Vision and Pattern Recognition (cs.CV); Computer Science - Computer Vision and Pattern Recognition; TP1-1185; recalibration; ranging; 01 natural sciences; Article; 0104 chemical sciences; self-driving vehicles; vidni stereo, rekalibracija, optimizacija, meritve razdalje, samovozeča vozila; info:eu-repo/classification/udc/004.8; vidni stereo; rekalibracija; optimizacija; optimization; visual stereo, recalibration, optimization, ranging, self-driving vehicles; visual stereo

Camera systems in autonomous vehicles are subject to various sources of anticipated and unanticipated mechanical stress (vibration, rough handling, collisions) in real-world conditions. Even moderate changes in camera geometry due to mechanical stress decalibrate multi-camera systems and corrupt downstream applications like depth perception. We propose an on-the-fly stereo recalibration method applicable in real-world autonomous vehicles. The method is comprised of two parts. First, in optimization step, external camera parameters are optimized with the goal to maximise the amount of recovered depth pixels. In the second step, external sensor is used to adjust the scaling of the optimized camera model. The method is lightweight and fast enough to run in parallel with stereo estimation, thus allowing an on-the-fly recalibration. Our extensive experimental analysis shows that our method achieves stereo reconstruction better or on par with manual calibration. If our method is used on a sequence of images, the quality of calibration can be improved even further.