OECS: Towards Online Extrinsics Correction for the Surround-view System

A typical surround-view system consists of four fisheye cameras. By performing an offline calibration that determines both the intrinsics and extrinsics of the system, surround-view images can be synthesized at runtime. However, poses of calibrated cameras sometimes may change. In such a case, if cameras’ extrinsics are not updated accordingly, observable geometric misalignment will appear in surround-views. Most existing solutions to this problem resort to re-calibration, which is quite cumbersome. Thus, how to correct cameras’extrinsics in an online manner without using re-calibration is still an open issue. In this paper, we attempt to propose a novel solution to this problem and the proposed solution is referred to as “Online Extrinsics Correction for the Surroundview system”, OECS for short. We first design a Bi-Camera error model, measuring the photometric discrepancy between two corresponding pixels on images captured by two adjacent cameras. Then, by minimizing the system’s overall Bi-Camera error, cameras’ extrinsics can be optimized and the optimization is conducted within a sparse direct framework. The efficacy and efficiency of OECS are validated by experiments. Data and source code used in this work are publicly available at https://z619850002.github.io/OECS_HomePage/.