Modeling a dynamic environment using a Bayesian multiple hypothesis approach

摘要

Dynamic world modeling requires the integration of multiple sensor observations obtained from multiple vehicle locations at different times. A crucial problem in this interpretation task is the presence of uncertainty in the origins of measurements (data association or correspondence uncertainty) as well as in the values of measurements (noise uncertainty). Almost all previous work in robotics has not distinguished between these two very different forms of uncertainty. In this paper we propose to model the uncertainty due to noise, e.g. the error in an object's position, by conventional covariance matrices. To represent the data association uncertainty, an hypothesis tree is constructed, the branches at any node representing different possible assignments of measurements to features. A rigorous Bayesian data association framework is then introduced that allows the probability of each hypothesis to be calculated. These probabilities can be used to guide an intelligent pruning strategy.