Improving condition- and environment-invariant place recognition with semantic place categorization

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dc.contributor.authorGarg, S.
dc.contributor.authorJacobson, A.
dc.contributor.authorKumar, S.
dc.contributor.authorMilford, M.
dc.contributor.conferenceIEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (24 Sep 2017 - 28 Sep 2017 : Vancouver Convention Centre, Vancouver, British Columbia, Canada)
dc.contributor.editorBicchi, A.
dc.contributor.editorOkamura, A.
dc.date.issued2017
dc.description.abstractThe place recognition problem comprises two distinct subproblems; recognizing a specific location in the world (“specific” or “ordinary” place recognition) and recognizing the type of place (place categorization). Both are important competencies for mobile robots and have each received significant attention in the robotics and computer vision community, but usually as separate areas of investigation. In this paper, we leverage the powerful complementary nature of place recognition and place categorization processes to create a new hybrid place recognition system that uses place context to inform place recognition. We show that semantic place categorization creates an informative natural segmenting of physical space that in turn enables significantly better place recognition performance in comparison to existing techniques. In particular, this new semantically-informed approach adds robustness to significant local changes within the environment, such as transitioning between indoor and outdoor environments or between dark and light rooms in a house, complementing the capabilities of current condition-invariant techniques that are robust to globally consistent change (such as day to night cycles). We perform experiments using 4 novel benchmark datasets and show that semantically-informed place recognition outperforms the previous state-of-the-art systems. Like it does for object recognition [1], we believe that semantics can play a key role in boosting conventional place recognition and navigation performance for robotic systems.
dc.description.statementofresponsibilitySourav Garg, Adam Jacobson, Swagat Kumar and Michael Milford
dc.identifier.citationProceedings of the ... IEEE/RSJ International Conference on Intelligent Robots and Systems. IEEE/RSJ International Conference on Intelligent Robots and Systems, 2017 / Bicchi, A., Okamura, A. (ed./s), vol.2017-September, pp.6863-6870
dc.identifier.doi10.1109/IROS.2017.8206608
dc.identifier.isbn9781538626825
dc.identifier.issn2153-0858
dc.identifier.issn2153-0866
dc.identifier.orcidGarg, S. [0000-0001-6068-3307]
dc.identifier.urihttps://hdl.handle.net/2440/138397
dc.language.isoen
dc.publisherIEEE
dc.relation.granthttp://purl.org/au-research/grants/arc/CE140100016
dc.relation.granthttp://purl.org/au-research/grants/arc/FT140101229
dc.relation.ispartofseriesIEEE International Conference on Intelligent Robots and Systems
dc.rights©2017 IEEE
dc.source.urihttps://ieeexplore.ieee.org/xpl/conhome/8119304/proceeding
dc.titleImproving condition- and environment-invariant place recognition with semantic place categorization
dc.typeConference paper
pubs.publication-statusPublished

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