Feature-Based Mapping Using Incremental Gaussian Mixture Models

This paper proposes a new algorithm for feature-based environment mapping where the environment is represented using multivariate Gaussian mixture models. This algorithm, which can be used either with sonar or laser range data, is able to create and maintain environment maps in real time using few m...

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Bibliographic Details
Main Authors: Heinen, M R, Engel, P M
Format: Conference Proceeding
Language:English
Subjects:
Computational modeling
Data models
Feature-based mapping
Gaussian mixture models
Incremental learning
Laser modes
Robot kinematics
Robot sensing systems
Semi-parametric methods
Sonar
Online Access:Request full text
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Summary:This paper proposes a new algorithm for feature-based environment mapping where the environment is represented using multivariate Gaussian mixture models. This algorithm, which can be used either with sonar or laser range data, is able to create and maintain environment maps in real time using few memory requirements. Moreover, it does not assume that the environment is composed by linear structures and allows computing the occupancy probabilities of any map position very fast and without introducing discretization errors. The experiments performed with the proposed model prototype show that it is able to build accurate environment representations using real data provided by a mobile robot.
DOI:10.1109/LARS.2010.13