Correcting Decalibration of Stereo Cameras in Self-Driving Vehicles

Sensors
Jon Muhovič, Janez Perš

Abstract

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.

References

Jun 27, 2008·IEEE Transactions on Pattern Analysis and Machine Intelligence·David Nistér
Jun 6, 2009·IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society·Thao DangChristoph Stiller
Apr 4, 2015·IEEE Transactions on Cybernetics·Matej KristanJanez Pers
Sep 21, 2016·Sensors·Basam MuslehArturo de la Escalera

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Citations

Apr 4, 2021·Sensors·De Jong YeongJoseph Walsh

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Software Mentioned

OpenCV

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