EOL

This post is an application of the EOL calibration described in the EOL calibration article. Detect Image Corners Detecting the corners of the fisheye images on its original image is challenging due to its severe distortion. So we resort to detecting the corners on the BEV image and then project the corners back onto the original image to further refine the corners. Set initial extrinsics (referring to installation parameters (angles and positions) or parameters from joint calibration with LiDAR), construct a 20m×20m grid with resolution of 0.01m in the ego coordinate system’s ground plane (z=0), and generate a BEV projected image; ...

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. ...