Using multiple Gaussian hypotheses to represent probability distributions for mobile robot localization
- 7 November 2002
- proceedings article
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- Vol. 2, 1036-1041
- https://doi.org/10.1109/robot.2000.844736
Abstract
A new mobile robot localization technique is pre- sented which uses multiple Gaussian hypotheses to rep- resent the probability distribution of the robots location in the environment. Sensor data is assumed to be pro- vided in the form of a Gaussian distribution over the space of robot poses. A tree of hypotheses is built, rep- resenting the possible data association histories for the system. Covariance intersection is used for the fusion o/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 o/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 demonstrat- ing the effectiveness of the scheme.Keywords
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