Autonomous active calibration of a dynamic camera cluster using next-best-view
Jason Rebello, Arun Das, Steven Waslander
Abstract
Dynamic camera cluster (DCC) calibration determines a time-varying set of extrinsic calibration transformations between cameras in a multi-camera cluster, where one or more cameras are mounted on actuated mechanisms. In this paper, we present a novel active vision approach for DCC calibration, which directly reduces the parameter uncertainty by selecting calibration measurements using an information theoretic next-best-view policy. Our system automatically selects the next best measurement for the calibration by determining the optimal actuator inputs which minimize the predicted covariance of the extrinsic parameters. We show that our method is able to successfully estimate calibration parameters up to a user specified accuracy with no manual excitation. We test our method in simulation on a variety of actuated mechanisms and validate the results on a real 3 axis gimbal, and demonstrate our approach is able to achieve accurate calibrations using fewer measurement sets when compared to existing approaches.
BibTeX
@inproceedings{iros2017_autonomousactive,
title = {Autonomous active calibration of a dynamic camera cluster using next-best-view},
author = {Jason Rebello and Arun Das and Steven Waslander},
booktitle = {IROS 2017},
year = {2017}
}