Stereo vision is a common sensing technique for mobile robots and is becoming more broadly used in automotive, industrial, entertainment, and consumer products. The quality of range data from a stereo system is highly dependent on the intrinsic and extrinsic calibration of the sensor head. Unfortunately, for deployed systems, drift in extrinsic calibration is nearly unavoidable. Thermal variation and cycling combined with shock and vibration can cause transitory or permanent changes in extrinsics that are not modeled accurately by a static calibration. As a result the quality of the sensor degrades significantly. We have developed a new approach that provides real-time continuous calibration updates to extrinsic parameters. Our approach optimizes the extrinsic parameters to reduce epipolar errors over one or multiple frames. A Kalman Filter is used to continually refine these parameter estimates and minimize inaccuracies resulting from visual feature noise and spurious feature matches between the left and right images. The extrinsic parameter updates can be used to re-rectify the stereo imagery. Thus, it serves as a pre-processing step for any stereo process ranging, from dense reconstruction to visual odometry. We have validated our system in a range of environments and stereo tasks and demonstrated it at the recent Computer Vision and Pattern Recognition conference. Significant improvements to stereo visual odometry and scene mapping accuracy were achieved for datasets collected using both custom built and commercial stereo heads.


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