2020/08/09 by Jeong-Kyun Lee, Young Ki Baik, Lee, Jeong-Kyun +5
Computer Science · Environmental Science · #Advanced Vision and Imaging #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Remote Sensing and LiDAR Applications #Remote Sensing in Agriculture
paper · pdf · doi:10.48550/arxiv.2008.03722
openalex publication_date 2020/08/09 · openalex created_date 2020/08/13 · openalex updated_date 2026/07/28
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.