Accurate calibration of multi-perspective cameras from a generalization of the hand-eye constraint
This addresses calibration issues for multi-perspective cameras in applications like smart vehicles and virtual/augmented reality, but it is incremental as it builds on existing hand-eye calibration with an external motion capture system.
The paper tackles the calibration of multi-perspective cameras, which is challenging due to large system sizes or lack of overlap, by extending the hand-eye calibration problem to jointly solve multi-eye-to-base problems in closed form. The method is shown to be highly efficient and accurate, outperforming existing closed-form alternatives in experiments.
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.