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Autonomous Robots

, Volume 32, Issue 4, pp 351–368 | Cite as

PLISS: labeling places using online changepoint detection

  • Ananth RanganathanEmail author
Article

Abstract

A shared vocabulary between humans and robots for describing spatial concepts is essential for effective human robot interaction. Towards this goal, we present a novel technique for place categorization from visual cues called PLISS (Place Labeling through Image Sequence Segmentation). PLISS is different from existing place categorization systems in two major ways—it inherently works on video and image streams rather than single images, and it can detect “unknown” place labels, i.e. place categories that it does not know about. PLISS uses changepoint detection to temporally segment image sequences which are subsequently labeled. Changepoint detection and labeling are performed inside a systematic probabilistic framework. Unknown place labels are detected by using a probabilistic classifier and keeping track of its label uncertainty. We present experiments and comparisons on the large and extensive VPC dataset. We also demonstrate results using models learned from images downloaded from Google’s image search.

Keywords

Place categorization Semantic mapping Computer vision Bayesian Probabilistic modeling Place recognition 

Supplementary material

(MOV 8.3 MB)

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Copyright information

© Springer Science+Business Media, LLC 2012

Authors and Affiliations

  1. 1.Honda Research Institute USA, IncMountain ViewUSA

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