NDLI: Bayesian inference in the space of topological maps

Content Provider	IEEE Xplore Digital Library
Author	Ranganathan, A. Menegatti, E. Dellaert, F.
Copyright Year	2004
Abstract	While probabilistic techniques have previously been investigated extensively for performing inference over the space of metric maps, no corresponding general-purpose methods exist for topological maps. We present the concept of probabilistic topological maps (PTMs), a sample-based representation that approximates the posterior distribution over topologies, given available sensor measurements. We show that the space of topologies is equivalent to the intractably large space of set partitions on the set of available measurements. The combinatorial nature of the problem is overcome by computing an approximate, sample-based representation of the posterior. The PTM is obtained by performing Bayesian inference over the space of all possible topologies, and provides a systematic solution to the problem of perceptual aliasing in the domain of topological mapping. In this paper, we describe a general framework for modeling measurements, and the use of a Markov-chain Monte Carlo algorithm that uses specific instances of these models for odometry and appearance measurements to estimate the posterior distribution. We present experimental results that validate our technique and generate good maps when using odometry and appearance, derived from panoramic images, as sensor measurements.
Sponsorship	IEEE Robotics and Automation Society
Starting Page	92
Ending Page	107
Page Count	16
File Size	1420161
File Format	PDF
ISSN	15523098
Volume Number	22
Issue Number	1
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2006-02-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Bayesian methods Robot sensing systems Topology Simultaneous localization and mapping Monte Carlo methods Intelligent robots Navigation Inference algorithms Image sensors Mobile robots topological maps Bayesian inference Markov-chain Monte Carlo (MCMC) mobile robots perceptual aliasing probability distributions sample-based representations
Content Type	Text
Resource Type	Article
Subject	Control and Systems Engineering Electrical and Electronic Engineering Computer Science Applications

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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