Simultaneous mapping and stereo extrinsic parameter calibration using GPS measurements

Stereo vision is useful for a variety of robotics tasks, such as navigation and obstacle avoidance. However, recovery of valid range data from stereo depends on accurate calibration of the extrinsic parameters of the stereo rig, i.e., the 6-DOF transform between the left and right cameras. Stereo self-calibration is possible, but, without additional information, the absolute scale of the stereo baseline cannot be determined. In this paper, we formulate stereo extrinsic parameter calibration as a batch maximum likelihood estimation problem, and use GPS measurements to establish the scale of both the scene and the stereo baseline. Our approach is similar to photogrammetric bundle adjustment, and closely related to many structure from motion algorithms. We present results from simulation experiments using a range of GPS accuracy levels; these accuracies are achievable by varying grades of commercially-available receivers. We then validate the algorithm using stereo and GPS data acquired from a moving vehicle. Our results indicate that the approach is promising.