Robust calibration of surround-view camera systems with Planar-Eccentric Constraint: A novel Apriltag approach

Abstract

This research tackles calibrating surround view camera systems for automated driving, where traditional methods require extensive manual measurements and precise conditions. We introduce a novel automatic calibration method that ensures portability and accuracy using only custom-designed calibration boards placed in the cameras’ common view. To address visual interference from extraneous objects, we design a unique board with AprilTag 2D codes, offering clear advantages over traditional checkerboards. Additionally, we’ve developed an Adaptive Angle Projection to counteract fisheye lens distortions. We also introduce a hierarchical pose optimization strategy that transitions from local to global, incorporating a “Planar-Eccentric constraint” to leverage the calibration board’s geometric positioning. Our method achieved 4.01 cm measurement error, with 0.075°angle error and 1.18 cm translation error. Our method’s efficacy and robustness were confirmed through extensive testing on an experimental vehicle and simulation dataset, proving its superiority in achieving precise calibration and measurement with minimal manual input.