User Feedback and Sample Weighting for Ill-Conditioned Hand-Eye Calibration

TL;DR

This paper introduces a method to automatically weight motion samples in hand-eye calibration to improve problem conditioning, and provides feedback mechanisms to avoid ill-conditioning during data collection.

Contribution

It presents a novel approach for weighting samples based on rotation axis density and estimates sensitivity to enhance calibration robustness.

Findings

Improved conditioning of calibration optimization.

Effective sample weighting based on rotation density.

User feedback mechanism to prevent ill-conditioning.

Abstract

Hand-eye calibration is an important and extensively researched method for calibrating rigidly coupled sensors, solely based on estimates of their motion. Due to the geometric structure of this problem, at least two motion estimates with non-parallel rotation axes are required for a unique solution. If the majority of rotation axes are almost parallel, the resulting optimization problem is ill-conditioned. In this paper, we propose an approach to automatically weight the motion samples of such an ill-conditioned optimization problem for improving the conditioning. The sample weights are chosen in relation to the local density of all available rotation axes. Furthermore, we present an approach for estimating the sensitivity and conditioning of the cost function, separated into the translation and the rotation part. This information can be employed as user feedback when recording the calibration data to prevent ill-conditioning in advance. We evaluate and compare our approach on artificially augmented data from the KITTI odometry dataset.