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A new mobile robot localization technique is presented which uses multiple Gaussian hypotheses to represent the probability distribution of the robot location in the environment. Sensor data is assumed to be provided in the form of a Gaussian distribution over the space of robot poses. A tree of hypotheses is built, representing the possible data association histories for the system. Covariance intersection is used for the fusion of the Gaussians whenever a data association decision is taken. However, such a tree can grow without bound and so rules are introduced for the elimination of the least likely hypotheses from the tree and for the proper re-distribution of their probabilities. This technique is applied to a feature-based mobile robot localization scheme and experimental results are given demonstrating the effectiveness of the scheme.
Austin et al. (2002) studied this question.