Extrinsic parameter self-calibration and nonlinear filtering for in-vehicle stereo vision systems at urban environments

Present work analyses the continuous self-calibration of extrinsic parameters of a stereo vision system for safe visual odometry applications in vehicles at urban environments. The calibration method determines the extrinsic parameters of a stereo vision system based on knowing the geometry of the ground in front of the cameras. The slight changes of the road profile cause variations in the extrinsic parameters of stereo rig that are necessary to filter and maintain between tolerance values. Then, height, pitch and roll parameters are filtered, to eliminate pose outliers of the stereo rig that appear when a vehicle is maneuvering. The reliable approach at urban environment is firstly composed of the calculation of the road profile slope, the theoretical horizon, and the slope of the straight line in the free map. Secondly, the nonlinear filtering is applied using Unscented Kalman Filter to improve the estimation of height, pitch and roll parameters.