Using Multiple Gaussian Hypotheses to Represent Probability Distributions for Mobile Robot Localization

A new mobile robot localization technique is presented which uses multiple Gaussian hypotheses to represent the probability distribution of the robots 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 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 demonstrating the effectiveness of the scheme. 1 I n t r o d u c t i o n In many areas it is desirable to be able to efficiently represent an arbitrary probability distribution. A number of techniques have been used for this, including grid-based approaches [1] and samplebased approaches [2, 3, 4]. However, these approaches tend to be computationally expensive. Grid-based approaches can be very wasteful, requiring computation of the probability even in areas where the probability is negligible. On the other hand, sample-based approximations require the computation of a significant number of samples and care must be taken when deciding the location of the samples to ensure that all of the significant areas of the distribution are sampled. Furthermore, extracting information such as the peak from grid-based or sample-based distributions can be computationally expensive. 0-7803-5886-4•00•510.00© 2000 IEEE 103 o m o u s S y s t e m s , of Techno logy ,