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Auteur Simon Lynen |
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Trajectory-based place-recognition for efficient large scale localization / Simon Lynen in International journal of computer vision, vol 124 n° 1 (August 2017)
[article]
Titre : Trajectory-based place-recognition for efficient large scale localization Type de document : Article/Communication Auteurs : Simon Lynen, Auteur ; Michael Bosse, Auteur ; Roland Siegwart, Auteur Année de publication : 2017 Article en page(s) : pp 49 – 64 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] alignement semi-dirigé
[Termes IGN] analyse comparative
[Termes IGN] appariement automatique
[Termes IGN] lieu géométrique
[Termes IGN] précision de localisation
[Termes IGN] reconnaissance automatiqueRésumé : (auteur) Place recognition is a core competency for any visual simultaneous localization and mapping system. Identifying previously visited places enables the creation of globally accurate maps, robust relocalization, and multi-user mapping. To match one place to another, most state-of-the-art approaches must decide a priori what constitutes a place, often in terms of how many consecutive views should overlap, or how many consecutive images should be considered together. Unfortunately, such threshold dependencies limit their generality to different types of scenes. In this paper, we present a placeless place recognition algorithm using a novel match-density estimation technique that avoids heuristically discretizing the space. Instead, our approach considers place recognition as a problem of continuous matching between image streams, automatically discovering regions of high match density that represent overlapping trajectory segments. The algorithm uses well-studied statistical tests to identify the relevant matching regions which are subsequently passed to an absolute pose algorithm to recover the geometric alignment. We demonstrate the efficiency and accuracy of our methodology on three outdoor sequences, including a comprehensive evaluation against ground-truth from publicly available datasets that shows our approach outperforms several state-of-the-art algorithms for place recognition. Furthermore we compare our overall algorithm to the currently best performing system for global localization and show how we outperform the approach on challenging indoor and outdoor datasets. Numéro de notice : A2017-399 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1007%2Fs11263-016-0947-9 En ligne : https://doi.org/10.1007/s11263-016-0947-9 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=85941
in International journal of computer vision > vol 124 n° 1 (August 2017) . - pp 49 – 64[article]