Bayesian Map Learning in Dynamic Environments (0)
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| Venue: | In Neural Info. Proc. Systems (NIPS |
| Citations: | 111 - 5 self |
BibTeX
@INPROCEEDINGS{Murphy_bayesianmap,
author = {Kevin Murphy},
title = {Bayesian Map Learning in Dynamic Environments},
booktitle = {In Neural Info. Proc. Systems (NIPS},
year = {},
pages = {1015--1021},
publisher = {MIT Press}
}
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Abstract
We show how map learning can be formulated as inference in a graphical model, which allows us to handle changing environments in a natural manner. We describe several different approximation schemes for the problem, and illustrate some results on a simulated grid-world with doors that can open and close. We close by briefly discussing how to learn more general models of (partially observed) environments, which can contain a variable number of objects with changing internal state. 1 Introduction Mobile robots need to navigate in dynamic environments: on a short time scale, obstacles, such as people, can appear and disappear, and on longer time scales, structural changes, such as doors opening and closing, can occur. In this paper, we consider how to create models of dynamic environments. In particular, we are interested in modeling the location of objects, which we can represent using a map. This enables the robot to perform path planning, etc. We propose a Bayesian approach in ...
