Article thumbnail

MoB01.4 Integration of Multiple Sensors using Binary Features in a Bernoulli Mixture Model

By F. Ferreira, V. Santos and J. Dias


Abstract — This article reports on the use of a Bernoulli Mixture model to integrate features extracted independently from two or more distinct sensors. 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). The correlation between the different features is captured by modeling the resultant FIM in terms of a Bernoulli Mixture Model. The integration of binary features from different sensors allows for good place recognition. The use of binary features also promises a much simpler integration of features from dissimilar sensors. I

Year: 2013
OAI identifier: oai:CiteSeerX.psu:
Provided by: CiteSeerX
Download PDF:
Sorry, we are unable to provide the full text but you may find it at the following location(s):
  • (external link)
  • (external link)
  • Suggested articles

    To submit an update or takedown request for this paper, please submit an Update/Correction/Removal Request.