A Probabilistic Approach to Concurrent Mapping and Localization for Mobile Robots


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Abstract

This paper addresses the problem of building large-scale geometric maps of indoor environments with mobile robots. It poses the map building problem as a constrained, probabilistic maximum-likelihood estimation problem. It then devises a practical algorithm for generating the most likely map from data, along with the most likely path taken by the robot. Experimental results in cyclic environments of size up to 80 × 25 m illustrate the appropriateness of the approach.

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