Robust place recognition within multi-sensor view sequences using Bernoulli mixture models

Filipe Ferreira, Vitor Santos, Jorge Dias

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Scopus citations


This article reports on the use of Hidden Markov Models to improve the results of Localization within a sequence of Sensor Views. Local image features (SIFT) and multiple types of features from a 2D laser range scan are all converted into binary form and integrated into a single, binary, Feature Incidence Matrix (FIM). To reduce the large dimensionality of the binary data, it is modeled in terms of a Bernoulli Mixture providing good results that were reported in an earlier presentation. We have improved the good performance of the approach by incorporating the Bernoulli mixture model inside a Bayesian Network Model, an HMM, that accumulates evidence as the robot travels along the environment.

Original languageBritish English
Title of host publication6th IFAC Symposium on Intelligent Autonomous Vehicles, IAV 200707 - Proceedings
Number of pages6
EditionPART 1
StatePublished - 2007

Publication series

NameIFAC Proceedings Volumes (IFAC-PapersOnline)
NumberPART 1
ISSN (Print)1474-6670


  • Bernoulli mixture model
  • Binary data
  • Dimensionality reduction
  • Expectation maximisation
  • Robot localization


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