Abstract
Navigation systems help users access unfamiliar environments. Current technological advancements enable users to encapsulate these systems in handheld devices, which effectively increases the popularity of navigation systems and the number of users. In indoor environments, lack of Global Positioning System (GPS) signals and line of sight with orbiting satellites makes navigation more challenging compared to outdoor environments. Radio frequency (RF) signals, computer vision, and sensor-based solutions are more suitable for tracking the users in indoor environments. This article provides a comprehensive summary of evolution in indoor navigation and indoor positioning technologies. In particular, the paper reviews different computer vision-based indoor navigation and positioning systems along with indoor scene recognition methods that can aid the indoor navigation. Navigation and positioning systems that utilize pedestrian dead reckoning (PDR) methods and various communication technologies, such as Wi-Fi, Radio Frequency Identification (RFID) visible light, Bluetooth and ultra-wide band (UWB), are detailed as well. Moreover, this article investigates and contrasts the different navigation systems in each category. Various evaluation criteria for indoor navigation systems are proposed in this work. The article concludes with a brief insight into future directions in indoor positioning and navigation systems.
Highlights
The term ‘navigation’ collectively represent tasks that include tracking the user’s position, planning feasible routes and guiding the user through the routes to reach the desired destination
We provide a summary of recent advancements and developments in the field of indoor navigation and positioning systems that utilize different types of approaches, such as Indoor positioning and wayfinding systems
Huang et al [62] developed an indoor positioning system called 3DLoc, which is a 3D feature-based indoor positioning system that can operate on handheld smart devices to locate the user in real time. This system solves the limitation that exists in previous indoor navigation systems based on sensors and feature matching (e.g., Scale Invariant Feature Transform (SIFT) and Speeded Up Robust Feature (SURF)), and it considers the 3D signature of pictures of places to recognize them with high accuracy
Summary
The term ‘navigation’ collectively represent tasks that include tracking the user’s position, planning feasible routes and guiding the user through the routes to reach the desired destination. Wi-Fi-based indoor navigation systems make use of RSS fingerprinting or triangulation or trilateration methods for positioning [15]. The proposed work focused on the development of three navigation systems that utilize image matching, QR code, and BLE beacons respectively for localizing the user and testing of the developed navigation system in the realtime indoor environment.
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