Part-Based Room Categorization for Household Service Robots

Peter Ursic, Rok Mandeljc, Ales Leonardis, Matej Kristan

Research output: Chapter in Book/Report/Conference proceedingConference contribution

8 Citations (Scopus)


A service robot that operates in a previously-unseen home environment should be able to recognize the functionality of the rooms it visits, such as a living room, a bathroom, etc. We present a novel part-based model and an approach for room categorization using data obtained from a visual sensor. Images are represented with sets of unordered parts that are obtained by object-agnostic region proposals, and encoded using state-of-the-art image descriptor extractor - a convolutional neural network (CNN). An approach is proposed that learns category-specific discriminative parts for the part-based model. The proposed approach was compared to the state-of-the-art CNN trained specifically for place recognition. Experimental results show that the proposed approach outperforms the holistic CNN by being robust to image degradation, such as occlusions, modifications of image scaling, and aspect changes. In addition, we report non-negligible annotation errors and image duplicates in a popular dataset for place categorization and discuss annotation ambiguities.
Original languageEnglish
Title of host publication2016 IEEE International Conference on Robotics and Automation (ICRA)
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
ISBN (Electronic)978-1-4673-8026-3
Publication statusPublished - 9 Jun 2016


Dive into the research topics of 'Part-Based Room Categorization for Household Service Robots'. Together they form a unique fingerprint.

Cite this