Accurate calibration of multi-perspective cameras from a generalization of the hand-eye constraint

TL;DR

This paper introduces a novel, efficient, and accurate method for calibrating multi-perspective cameras using an external motion capture system, extending hand-eye calibration to multi-eye-to-base problems.

Contribution

It extends the hand-eye calibration framework to jointly solve multi-eye-to-base problems in closed form, demonstrating equivalence to multi-eye-in-hand calibration.

Findings

Method outperforms existing closed-form solutions in accuracy

Approach is highly efficient and practical

Validates the approach with experiments showing superior results

Abstract

Multi-perspective cameras are quickly gaining importance in many applications such as smart vehicles and virtual or augmented reality. However, a large system size or absence of overlap in neighbouring fields-of-view often complicate their calibration. We present a novel solution which relies on the availability of an external motion capture system. Our core contribution consists of an extension to the hand-eye calibration problem which jointly solves multi-eye-to-base problems in closed form. We furthermore demonstrate its equivalence to the multi-eye-in-hand problem. The practical validity of our approach is supported by our experiments, indicating that the method is highly efficient and accurate, and outperforms existing closed-form alternatives.