Look no deeper: Recognizing places from opposing viewpoints under varying scene appearance using single-view depth estimation

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dc.contributor.authorGarg, S.
dc.contributor.authorBabu, M.V.
dc.contributor.authorDharmasiri, T.
dc.contributor.authorHausler, S.
dc.contributor.authorSuenderhauf, N.
dc.contributor.authorKumar, S.
dc.contributor.authorDrummond, T.
dc.contributor.authorMilford, M.
dc.contributor.conferenceInternational Conference on Robotics and Automation (ICRA) (20 May 2019 - 24 May 2019 : Montreal, Canada)
dc.contributor.editorHoward, A.
dc.contributor.editorAlthoefer, K.
dc.contributor.editorArai, F.
dc.contributor.editorArrichiello, F.
dc.contributor.editorCaputo, B.
dc.contributor.editorCastellanos, J.
dc.contributor.editorHauser, K.
dc.contributor.editorIsler, V.
dc.contributor.editorKim, J.
dc.contributor.editorLiu, H.
dc.contributor.editorOh, P.
dc.contributor.editorSantos, V.
dc.contributor.editorScaramuzza, D.
dc.contributor.editorUde, A.
dc.contributor.editorVoyles, R.
dc.contributor.editorYamane, K.
dc.contributor.editorOkamura, A.
dc.date.issued2019
dc.description.abstractVisual place recognition (VPR) - the act of recognizing a familiar visual place - becomes difficult when there is extreme environmental appearance change or viewpoint change. Particularly challenging is the scenario where both phenomena occur simultaneously, such as when returning for the first time along a road at night that was previously traversed during the day in the opposite direction. While such problems can be solved with panoramic sensors, humans solve this problem regularly with limited field-of-view vision and without needing to constantly turn around. In this paper, we present a new depth- and temporal-aware visual place recognition system that solves the opposing viewpoint, extreme appearancechange visual place recognition problem. Our system performs sequence-to-single frame matching by extracting depth-filtered keypoints using a state-of-the-art depth estimation pipeline, constructing a keypoint sequence over multiple frames from the reference dataset, and comparing these keypoints to the keypoints extracted from a single query image. We evaluate the system on a challenging benchmark dataset and show that it consistently outperforms state-of-the-art techniques. We also develop a range of diagnostic simulation experiments that characterize the contribution of depth-filtered keypoint sequences with respect to key domain parameters including the degree of appearance change and camera motion.
dc.description.statementofresponsibilitySourav Garg, Madhu Babu V, Thanuja Dharmasiri, Stephen Hausler, Niko Suenderhauf, Swagat Kumar, Tom Drummond, Michael Milford
dc.identifier.citationIEEE International Conference on Robotics and Automation, 2019 / Howard, A., Althoefer, K., Arai, F., Arrichiello, F., Caputo, B., Castellanos, J., Hauser, K., Isler, V., Kim, J., Liu, H., Oh, P., Santos, V., Scaramuzza, D., Ude, A., Voyles, R., Yamane, K., Okamura, A. (ed./s), vol.2019-May, pp.4916-4923
dc.identifier.doi10.1109/ICRA.2019.8794178
dc.identifier.isbn9781538660263
dc.identifier.issn1050-4729
dc.identifier.issn2577-087X
dc.identifier.orcidGarg, S. [0000-0001-6068-3307]
dc.identifier.urihttps://hdl.handle.net/2440/138398
dc.language.isoen
dc.publisherIEEE
dc.publisher.placePiscataway, NJ, USA
dc.relation.granthttp://purl.org/au-research/grants/arc/FT140101229
dc.relation.ispartofseriesIEEE International Conference on Robotics and Automation (ICRA)
dc.rights©2019 IEEE
dc.source.urihttps://ieeexplore.ieee.org/xpl/conhome/8780387/proceeding
dc.titleLook no deeper: Recognizing places from opposing viewpoints under varying scene appearance using single-view depth estimation
dc.typeConference paper
pubs.publication-statusPublished

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