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).
Highlights
In recent years, the field of indoor localization has increased in popularity due to both the increasing number of applications [1] in domains such as surveillance [2], navigation [3], robotics [4,5,6], and Augmented Reality (AR) [7] and the many proposed solutions that differ in terms of the devices used for tracking, the type of sensor data, and the localization algorithms.This paper focuses on computer vision based localization methods; the solutions presented are based on input from cameras
Since surveillance cameras and smartphone cameras represent a commodity currently, researchers have developed a plethora of indoor localization solutions based on visual input
This paper offers an overview of the computer vision based indoor localization domain, discussing applications areas, commercial solutions, and benchmarks and presenting some of the most relevant contributions in the area
Summary
The field of indoor localization has increased in popularity due to both the increasing number of applications [1] in domains such as surveillance [2], navigation (both assistive and general purpose) [3], robotics [4,5,6], and Augmented Reality (AR) [7] and the many proposed solutions that differ in terms of the devices used for tracking, the type of sensor data, and the localization algorithms.This paper focuses on computer vision based localization methods; the solutions presented are based on input from cameras. The field of indoor localization has increased in popularity due to both the increasing number of applications [1] in domains such as surveillance [2], navigation (both assistive and general purpose) [3], robotics [4,5,6], and Augmented Reality (AR) [7] and the many proposed solutions that differ in terms of the devices used for tracking, the type of sensor data, and the localization algorithms. Most navigation systems use cameras carried by the subject, which represents the mobile entity (e.g., person, robot) that requires positioning or tracking, as illustrated in the left-hand side of Figure 1. The other type of solutions uses an infrastructure of static cameras positioned at known locations throughout the building to track the subject, as shown in the right-hand side of Figure 1. The vision based localization systems use 2D or 3D cameras (e.g., stereo, depth, RGB-D cameras) and perform the localization by identifying artificial markers The cameras are used in combination with other sensors such as WiFi, beacon, or inertial sensors [1]
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