NDLI: Sequence searching with deep-learnt depth for condition- and viewpoint-invariant route-based place recognition

Content Provider	IEEE Xplore Digital Library
Author	Milford, M. Lowry, S. Sunderhauf, N. Shirazi, S. Pepperell, E. Upcroft, B. Shen, C. Lin, G. Liu, F. Cadena, C. Reid, I.
Copyright Year	2015
Description	Author affiliation: Australian Centre for Robotic Vision, Univ. of Adelaide, Adelaide, SA, Australia (Shen, C.; Lin, G.; Liu, F.; Cadena, C.; Reid, I.) \|\| Australian Centre for Robotic Vision, Queensland Univ. of Technol. Australia, QLD, Australia (Milford, M.; Lowry, S.; Sunderhauf, N.; Shirazi, S.; Pepperell, E.; Upcroft, B.)
Abstract	Vision-based localization on robots and vehicles remains unsolved when extreme appearance change and viewpoint change are present simultaneously. The current state of the art approaches to this challenge either deal with only one of these two problems; for example FAB-MAP (viewpoint invariance) or SeqSLAM (appearance-invariance), or use extensive training within the test environment, an impractical requirement in many application scenarios. In this paper we significantly improve the viewpoint invariance of the SeqSLAM algorithm by using state-of-the-art deep learning techniques to generate synthetic viewpoints. Our approach is different to other deep learning approaches in that it does not rely on the ability of the CNN network to learn invariant features, but only to produce“good enough” depth images from day-time imagery only. We evaluate the system on a new multi-lane day-night car dataset specifically gathered to simultaneously test both appearance and viewpoint change. Results demonstrate that the use of synthetic viewpoints improves the maximum recall achieved at 100% precision by a factor of 2.2 and maximum recall by a factor of 2.7, enabling correct place recognition across multiple road lanes and significantly reducing the time between correct localizations.
Starting Page	18
Ending Page	25
File Size	821584
Page Count	8
File Format	PDF
ISSN	21607516
e-ISBN	9781467367592
DOI	10.1109/CVPRW.2015.7301395
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-06-07
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Training Image recognition Computational modeling Estimation Trajectory Robots Vehicles
Content Type	Text
Resource Type	Article

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