Categorization of indoor places by combining local binary pattern histograms of range and reflectance data from laser range finders

Oscar Martinez Mozos, Hitoshi Mizutani, Hojung Jung, Ryo Kurazume, Tsutomu Hasegawa

Research output: Contribution to journalArticlepeer-review

13 Citations (Scopus)

Abstract

This paper presents an approach to categorize typical places in indoor environments using 3D scans provided by a laser range finder. Examples of such places are offices, laboratories, or kitchens. In our method, we combine the range and reflectance data from the laser scan for the final categorization of places. Range and reflectance images are transformed into histograms of local binary patterns and combined into a single feature vector. This vector is later classified using support vector machines. The results of the presented experiments demonstrate the capability of our technique to categorize indoor places with high accuracy. We also show that the combination of range and reflectance information improves the final categorization results in comparison with a single modality.

Original languageEnglish
Pages (from-to)1455-1464
Number of pages10
JournalAdvanced Robotics
Volume27
Issue number18
DOIs
Publication statusPublished - Dec 1 2013

All Science Journal Classification (ASJC) codes

  • Software
  • Control and Systems Engineering
  • Human-Computer Interaction
  • Hardware and Architecture
  • Computer Science Applications

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