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Sensors 2017, 17(11), 2442; doi:10.3390/s17112442

Visual Localization across Seasons Using Sequence Matching Based on Multi-Feature Combination

Le2i FRE2005, CNRS, Arts et Métiers, UBFC, Université de technologie de Belfort-Montbéliard, Belfort 90000, France
The paper is an an expanded version of a conference paper: Visual localization using sequence matching based on multi-feature combination. In Advanced Concepts for Intelligent Vision Systems (ACIVS 2016), Lecture Notes in Computer Science (LNCS), Lecce, Italy, October 2016.
Author to whom correspondence should be addressed.
Received: 29 August 2017 / Revised: 18 October 2017 / Accepted: 19 October 2017 / Published: 25 October 2017
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Visual localization is widely used in autonomous navigation system and Advanced Driver Assistance Systems (ADAS). However, visual-based localization in seasonal changing situations is one of the most challenging topics in computer vision and the intelligent vehicle community. The difficulty of this task is related to the strong appearance changes that occur in scenes due to weather or season changes. In this paper, a place recognition based visual localization method is proposed, which realizes the localization by identifying previously visited places using the sequence matching method. It operates by matching query image sequences to an image database acquired previously (video acquired during traveling period). In this method, in order to improve matching accuracy, multi-feature is constructed by combining a global GIST descriptor and local binary feature CSLBP (Center-symmetric local binary patterns) to represent image sequence. Then, similarity measurement according to Chi-square distance is used for effective sequences matching. For experimental evaluation, the relationship between image sequence length and sequences matching performance is studied. To show its effectiveness, the proposed method is tested and evaluated in four seasons outdoor environments. The results have shown improved precision–recall performance against the state-of-the-art SeqSLAM algorithm. View Full-Text
Keywords: visual localization; sequence matching; multi-feature combination; place recognition; binary features visual localization; sequence matching; multi-feature combination; place recognition; binary features

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Qiao, Y.; Cappelle, C.; Ruichek, Y. Visual Localization across Seasons Using Sequence Matching Based on Multi-Feature Combination. Sensors 2017, 17, 2442.

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