Colakovic-Benceric, Marta (University of Zagreb), Markovic, Ivan (University of Zagreb Faculty of Electrical Engineering and Compu), Bonsignorio, Fabio (University of Zagreb), Petrovic, Ivan (University of Zagreb)

Uncertainty-Aware Hand-Eye Decalibration Detection Via the Gauss-Helmert Model

Scheduled for presentation during the Regular Session "Modeling, Identification, and Estimation" (WeCT2), Wednesday, July 16, 2025, 17:10−17:30, Room 106

Joint 10th IFAC Symposium on Mechatronic Systems and 14th Symposium on Robotics, July 15-18, 2025, Paris, France

This information is tentative and subject to change. Compiled on July 16, 2025

Abstract

Detecting decalibration is crucial to maintain perception accuracy and prevent performance degradation in long-term autonomous operations, where environmental changes, mechanical wear, and external forces can compromise sensor alignment. In this paper, we propose a motion-based framework that leverages uncertainty-aware optimization for accurate decalibration detection and correction. Key contributions include a batch calibration procedure based on the Gauss-Helmert model with variance component estimation and a probabilistic detection criterion using the Mahalanobis distance. Finally, we develop an online detection system employing a sliding-window approach for continuous monitoring, demonstrating particularly rapid response to rotational misalignment.