Online Extrinsic Camera Calibration for Temporally Consistent IPM Using Lane Boundary Observations with a Lane Width Prior
This addresses the problem of accurate and stable camera calibration for autonomous driving systems, though it is incremental as it builds on existing lane-based methods.
The paper tackles online extrinsic camera calibration for generating temporally consistent bird's-eye-view images in driving scenes, achieving improved accuracy by sequentially updating parameters using lane boundary observations and a lane width prior with extended Kalman filtering.
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 in sequential driving scene images. The proposed method estimates the extrinsic camera parameters in two steps: 1) pitch and yaw angles are estimated simultaneously using a vanishing point computed from a set of lane boundary observations, and then 2) roll angle and camera height are computed by minimizing difference between lane width observations and a lane width prior. The extrinsic camera parameters are sequentially updated using extended Kalman filtering (EKF) and are finally used to generate a temporally consistent bird-eye-view (BEV) image by inverse perspective mapping (IPM). We demonstrate the superiority of the proposed method in synthetic and real-world datasets.