A Comprehensive Survey of Indoor Localization Methods Based on Computer Vision

Abstract

Computer vision based indoor localization methods use either an infrastructure of static cameras to track mobile entities (e.g., people, robots) or cameras attached to the mobile entities. Methods in the first category employ object tracking, while the others map images from mobile cameras with images acquired during a configuration stage or extracted from 3D reconstructed models of the space. This paper offers an overview of the computer vision based indoor localization domain, presenting application areas, commercial tools, existing benchmarks, and other reviews. It provides a survey of indoor localization research solutions, proposing a new classification based on the configuration stage (use of known environment data), sensing devices, type of detected elements, and localization method. It groups 70 of the most recent and relevant image based indoor localization methods according to the proposed classification and discusses their advantages and drawbacks. It highlights localization methods that also offer orientation information, as this is required by an increasing number of applications of indoor localization (e.g., augmented reality).

A Feed-Forward Neural Network Approach for Energy-Based Acoustic Source Localization

“…More specifically, ANNs can behave as universal approximators, and almost automatically discover features relevant to the localization problem by exploiting the increased amount of sensor data and computational power on an offline stage. The localization problem is treated as a regression one, where distances are learned, mapping coordinates in a search-space, having modest or nonexistent physical knowledge intrinsic to the problem under scrutiny [42][43][44][45].…”

Designing a Simple Fiducial Marker for Localization in Spatial Scenes Using Neural Networks

“…Shabalina et al [ 26 ] compared ARTag, AprilTag, and CALTag. In 2020, Morar et al prepared a comprehensive study that evaluated many systems in an indoor environment [ 27 ]. Some authors have theoretically focused on designing the best marker systems, e.g., [ 28 , 29 ].…”

Catiloc: Camera Image Transformer for Indoor Localization

“…There are plenty of valuable research works performed to address the indoor localization problem [1]. Some of these works, develop their systems based on processing of the information they can obtain using a distributed set of sensors and devices in the area of interest, such as WiFi and Bluetooth [2][3][4].…”

“…Pioneering by PoseNet [8], most of delicate techniques in this category incorporate the geometrical information [9], or even the perceptual and temporal features to improve the precision [10][11][12]. The reader is strongly urged to review the related works for a more complete picture of the field [1,13,14].…”