A Two-Step Optimization for Extrinsic Calibration of Multiple Camera System (MCS) Using Depth-Weighted Normalized Points

Abstract

A multiple camera system (MCS), which allows only a limited common field-of-view between adjacent cameras, has been chiefly calibrated using a typical 2D-3D point correspondence. However, the correspondence contains a potential instability by which the calibration result can diverge, and the instability has not been studied nor overcome before. We propose a MCS extrinsic calibration method with high robustness and accuracy based on a two-step optimization strategy. Using depth-weighted normalized points, we develop two novel types of point correspondence as follows. The 1st correspondence, F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">cb</sub> , aims to robustly estimate the MCS extrinsic parameters by overcoming the potential instability that exists in the typical 2D-3D point correspondence. The 2nd correspondence, F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">cc</sub> , aims to refine the MCS extrinsic parameters by using the direct relation between adjacent cameras. In the simulation, we validated the robustness and high accuracy of the proposed method. We validated its high precision in the field test. In both the simulation and the field test, our method was compared with the state-of-the-art method.