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

• Published in: Pattern recognition Vol. 74; pp. 90 - 109
• Main Authors: Piasco, Nathan, Sidibé, Désiré, Demonceaux, Cédric, Gouet-Brunet, Valérie
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01-02-2018; Elsevier
• Subjects: Camera relocalisation; Computer Science; Computer Vision and Pattern Recognition; Image Processing; Image-based localization; Pose estimation; Robotics; Signal and Image Processing; Visual geo-localization; Visual geo-localization; Image-based localization; Pose estimation; Camera relocalisation; Camera Relocalisation
• ISSN: 0031-3203; 1873-5142
• DOI: 10.1016/j.patcog.2017.09.013

•Comprehensive review of recent methods used for the task of Visual-Based Localization.•Explicit classification on two main categorizes of state-of-the-art works.•Discussion on the strengths and weaknesses of reviewed methods according to the type of data involved.
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.