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Article Dans Une Revue Pattern Recognition Année : 2018

A survey on Visual-Based Localization: On the benefit of heterogeneous data

Résumé

We are surrounded by plenty of information about our environment. From these multiple sources, numerous data could be extracted: set of images, 3D model, coloured points cloud... When classical localization devices failed (e.g. GPS sensor in cluttered environments), aforementioned data could be used within a localization framework. This is called Visual Based Localization (VBL). Due to numerous data types that can be collected from a scene, VBL encompasses a large amount of different methods. This paper presents a survey about recent methods that localize a visual acquisition system according to a known environment. We start by categorizing VBL methods into two distinct families: indirect and direct localization systems. As the localization environment is almost always dynamic, we pay special attention to methods designed to handle appearances changes occurring in a scene. Thereafter, we highlight methods exploiting heterogeneous types of data. Finally, we conclude the paper with a discussion on promising trends that could permit to a localization system to reach high precision pose estimation within an area as large as possible.
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Dates et versions

hal-01744680 , version 1 (27-03-2018)

Identifiants

Citer

Nathan Piasco, Désiré Sidibé, Cédric Demonceaux, Valérie Gouet-Brunet. A survey on Visual-Based Localization: On the benefit of heterogeneous data. Pattern Recognition, 2018, 74, pp.90 - 109. ⟨10.1016/j.patcog.2017.09.013⟩. ⟨hal-01744680⟩
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