On comparing statistical and set-based methods in sensor data fusion

Abstract

We compare the theoretical and practical considerations of two common sensor data fusion methodologies: set-based and statistically based parameter estimation. We first examine their convergence behavior for a variety of simulated problems. We then describe robot localization systems implemented using both methods and compare their performance. Our conclusion is that set-based methods have performance that sometimes exceeds that of statistical methods, although this result is highly problem dependent. We then characterize these problem dependencies. 1