论文标题
在线外部摄像机校准，用于使用车道宽度的车道边界观测到暂时一致的IPM
Online Extrinsic Camera Calibration for Temporally Consistent IPM Using Lane Boundary Observations with a Lane Width Prior
论文作者
论文摘要
在本文中，我们提出了一种用于在线外部摄像机校准的方法，即估算音高，偏航，滚动角度和摄像机的高度在顺序驾驶场景中的路面图像中。所提出的方法分为两个步骤估算外部摄像机参数：1）使用从一组车道边界观测值计算的消失点同时估算俯仰和偏航角，然后2）通过最小化车道宽度观测和车道宽度之间的差异来计算滚动角度和摄像机的高度。使用扩展的卡尔曼过滤（EKF）依次更新外部摄像机参数，并最终通过反视角映射（IPM）来生成时间一致的鸟眼视图（BEV）图像。我们证明了在合成和现实世界数据集中提出的方法的优越性。
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.
