A survey on Visual-Based Localization: On the benefit of heterogeneous data. (February 2018) - Piasco, Nathan; Sidibé, Désiré; Demonceaux, Cédric; Gouet-Brunet, Valérie
Highlights: 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. Abstract: 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.