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Incremental feature-based mapping from sonar data using Gaussian mixture models

Published: 21 March 2011 Publication History

Abstract

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 using sonar data show that it is able to build accurate environment representations using noisy data provided by a mobile robot.

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Cited By

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  • (2018)Feature Extraction and Mapping Construction for Mobile Robot via Ultrasonic MDP and Fuzzy ModelSensors10.3390/s1811367318:11(3673)Online publication date: 29-Oct-2018
  • (2012)Learning Abstract Behaviors with the Hierarchical Incremental Gaussian Mixture NetworkProceedings of the 2012 Brazilian Symposium on Neural Networks10.1109/SBRN.2012.30(131-135)Online publication date: 20-Oct-2012
  • (2012)Using a Gaussian mixture neural network for incremental learning and roboticsThe 2012 International Joint Conference on Neural Networks (IJCNN)10.1109/IJCNN.2012.6252399(1-8)Online publication date: Jun-2012

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cover image ACM Conferences
SAC '11: Proceedings of the 2011 ACM Symposium on Applied Computing
March 2011
1868 pages
ISBN:9781450301138
DOI:10.1145/1982185
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 21 March 2011

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Author Tags

  1. Bayesian methods
  2. Gaussian mixture models
  3. feature-based mapping
  4. incremental learning
  5. semi-parametric methods

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SAC'11: The 2011 ACM Symposium on Applied Computing
March 21 - 24, 2011
TaiChung, Taiwan

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Cited By

View all
  • (2018)Feature Extraction and Mapping Construction for Mobile Robot via Ultrasonic MDP and Fuzzy ModelSensors10.3390/s1811367318:11(3673)Online publication date: 29-Oct-2018
  • (2012)Learning Abstract Behaviors with the Hierarchical Incremental Gaussian Mixture NetworkProceedings of the 2012 Brazilian Symposium on Neural Networks10.1109/SBRN.2012.30(131-135)Online publication date: 20-Oct-2012
  • (2012)Using a Gaussian mixture neural network for incremental learning and roboticsThe 2012 International Joint Conference on Neural Networks (IJCNN)10.1109/IJCNN.2012.6252399(1-8)Online publication date: Jun-2012

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