Unsupervised learning of spatial-temporal models of objects in a long-term autonomy scenario
2015 (English)In: 2015 IEEE/RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS (IROS), IEEE , 2015, 5678-5685 p.Conference paper (Refereed)Text
We present a novel method for clustering segmented dynamic parts of indoor RGB-D scenes across repeated observations by performing an analysis of their spatial-temporal distributions. We segment areas of interest in the scene using scene differencing for change detection. We extend the Meta-Room method and evaluate the performance on a complex dataset acquired autonomously by a mobile robot over a period of 30 days. We use an initial clustering method to group the segmented parts based on appearance and shape, and we further combine the clusters we obtain by analyzing their spatial-temporal behaviors. We show that using the spatial-temporal information further increases the matching accuracy.
Place, publisher, year, edition, pages
IEEE , 2015. 5678-5685 p.
, IEEE International Conference on Intelligent Robots and Systems, ISSN 2153-0858
Computer Vision and Robotics (Autonomous Systems)
IdentifiersURN: urn:nbn:se:kth:diva-185111DOI: 10.1109/IROS.2015.7354183ISI: 000371885405119ScopusID: 2-s2.0-84958153798ISBN: 978-1-4799-9994-1OAI: oai:DiVA.org:kth-185111DiVA: diva2:918755
IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), SEP 28-OCT 02, 2015, Hamburg, GERMANY
QC 201604112016-04-112016-04-112016-04-12Bibliographically approved