A Deep Dive into End-of-Line Camera Extrinsic Calibration for Autonomous Vehicles
This article develops end-of-line (EOL) camera extrinsic calibration and calibration-board placement for autonomous-vehicle camera rigs, including bird’s-eye-view applications. There are two linked optimization problems: estimate camera and board poses from measurements, then choose physical board positions and orientations that make the camera estimates more precise. Camera intrinsics are assumed known, and absolute extrinsics require a world-frame anchor. The presentation follows this order: General calibration problem formulation: variables, coordinate conventions, measurements, priors, and identifiability. Solver: sparse factor-graph bundle adjustment for the pose estimates. Board-position and orientation optimization formulation: camera uncertainty, design variables, constraints, and optimization methods. Experiments from simple to complex: one camera and one board, two-camera cases, a multi-camera workspace, and a vehicle rig. Geometric preprocessing, ray-plane derivations, and optional design formulations are collected in appendices. The detailed ADMM formulation and solver benchmarks remain in the independent comparison. ...