A Consensus-based Approach for Estimating the Observation Likelihood of Accurate Range Sensors

One of the main elements of probabilistic localization and SLAM is the probabilistic sensor model (also known as the observation likelihood function). However, when dealing with very accurate sensors like laser range scanners, most approaches artificially inflate the uncertainty in the range measurements and assume conditional independence between the individual ranges of the scan to compute this likelihood function. In this paper we propose an alternative method where each sample in the scan can contribute an accurate estimation according to both its real uncertainty and its compatible correspondences with a given map. These likelihood values of individual measurements are fused via a linear opinion pool (LOP), a method from consensus theory. Our approach results in a more precise likelihood function than others and excels in robustness in dynamic environments. To validate our research we provide systematic comparisons with other proposals in the context of localization with particle filters.