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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.

We address the problem of optical decalibration in mobile stereo camera setups, especially in context of autonomous vehicles. In real world conditions, an optical system is subject to various sources of anticipated and unanticipated mechanical stress (vibration, rough handling, collisions). Mechanical stress changes the geometry between the cameras that make up the stereo pair, and as a consequence, the pre-calculated epipolar geometry is no longer valid. Our method is based on optimization of camera geometry parameters and plugs directly into the output of the stereo matching algorithm. Therefore, it is able to recover calibration parameters on image pairs obtained from a decalibrated stereo system with minimal use of additional computing resources. The number of successfully recovered depth pixels is used as an objective function, which we aim to maximize. Our simulation confirms that the method can run constantly in parallel to stereo estimation and thus help keep the system calibrated in real time. Results confirm that the method is able to recalibrate all the parameters except for the baseline distance, which scales the absolute depth readings. However, that scaling factor could be uniquely determined using any kind of absolute range finding methods (e.g. a single beam time-of-flight sensor).