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In this paper, we propose a method for online extrinsic camera calibration,i.e., estimating pitch, yaw, roll angles and camera height from road surface insequential driving scene images. The proposed method estimates the extrinsiccamera parameters in two steps: 1) pitch and yaw angles are estimatedsimultaneously using a vanishing point computed from a set of lane boundaryobservations, and then 2) roll angle and camera height are computed byminimizing difference between lane width observations and a lane width prior.The extrinsic camera parameters are sequentially updated using extended Kalmanfiltering (EKF) and are finally used to generate a temporally consistentbird-eye-view (BEV) image by inverse perspective mapping (IPM). We demonstratethe superiority of the proposed method in synthetic and real-world datasets.

Online Extrinsic Camera Calibration for Temporally Consistent IPM Using Lane Boundary Observations with a Lane Width Prior