Online Camera Pose Optimization for the Surround-view System
Xiao Liu, Lin Zhang, Ying Shen, Shaoming Zhang, Shengjie Zhao · 2019
Surround-view system is an important information medium for drivers to monitor the driving environment. A typical surround-view system consists of four to six fish-eye cameras arranged around the vehicle. From these camera inputs, a top-down image of the ground around the vehicle, namely the surround-view image can be generated with well calibrated camera poses. Although existing surround-view system solutions can estimate camera poses accurately in off-line environment, how to correct the camera poses' change in online environment is still an open issue. In this paper, we propose a camera pose optimization method for surround-view system in online environment. Our method consists of two models: Ground Model and Ground-Camera Model, both of which correct the camera poses by minimizing photometric errors between ground projections of adjacent cameras. Experiments show that our method can effectively correct the geometric misalignment of the surround-view image caused by camera poses' change. Since our method is highly automated with low requirement of calibration site and manual operation, it has a wide range of applications and is convenient for the end-users. To make the results reproducible, the source code is publicly available at https://cslinzhang.github.io/CamPoseOpt/.