Robust Covariance Estimation for Data Fusion From Multiple Sensors

This paper addresses the robust estimation of a covariance matrix to express uncertainty when fusing information from multiple sensors. This is a problem of interest in multiple domains and applications, namely, in robotics. This paper discusses the use of estimators using explicit measurements from the sensors involved versus estimators using only covariance estimates from the sensor models and navigation systems. Covariance intersection and a class of orthogonal Gnanadesikan-Kettenring estimators are compared using the 2-norm of the estimates. A Monte Carlo simulation of a typical mapping experiment leads to conclude that covariance estimation systems with a hybrid of the two estimators may yield the best results.