Leveraging Visual Place Recognition to Improve Indoor Positioning with Limited Availability of WiFi Scans

Abstract

WiFi-based fingerprinting is promising for practical indoor localization with smartphones because this technique provides absolute estimates of the current position, while the WiFi infrastructure is ubiquitous in the majority of indoor environments. However, the application of WiFi fingerprinting for positioning requires pre-surveyed signal maps and is getting more restricted in the recent generation of smartphones due to changes in security policies. Therefore, we sought new sources of information that can be fused into the existing indoor positioning framework, helping users to pinpoint their position, even with a relatively low-quality, sparse WiFi signal map. In this paper, we demonstrate that such information can be derived from the recognition of camera images. We present a way of transforming qualitative information of image similarity into quantitative constraints that are then fused into the graph-based optimization framework for positioning together with typical pedestrian dead reckoning (PDR) and WiFi fingerprinting constraints. Performance of the improved indoor positioning system is evaluated on different user trajectories logged inside an office building at our University campus. The results demonstrate that introducing additional sensing modality into the positioning system makes it possible to increase accuracy and simultaneously reduce the dependence on the quality of the pre-surveyed WiFi map and the WiFi measurements at run-time.

A Sustainable, Interactive Elderly Healthcare System for Nursing Homes: An Interdisciplinary Design

“…Satellite signals cannot easily penetrate obstacles (Nowicki, M.R. et al [33]), so indoor-positioning technology is required to provide positioning services in nursing homes. Most indoor-positioning systems are based on wireless networks, such as BLE, ZigBee, and Radio Frequency Identification (RFID) (Tao, Y.H.…”

A WiFi Indoor Location Tracking Algorithm Based on Improved Weighted K Nearest Neighbors and Kalman Filter

“…Enhancing the accuracy of WiFi positioning, which remains relatively low, poses the greatest challenge. The location-based fingerprint method in WiFi positioning is categorized into three groups: deterministic methods [13], probabilistic methods [14], and deep learning methods [15]. Despite their unparalleled accuracy, deep learning methods are inherently complex, requiring significant computational resources.…”

Snake optimizer LSTM-based UWB positioning method for unmanned crane

“…While Wi-Fi positioning has gained popularity, its accuracy remains relatively low. Wi-Fi fingerprint-based positioning methods require extensive signal mapping prior to deployment, making them impractical for unmanned driving cranes [6][7][8]. Bluetooth positioning allows for short-range reception of information but suffers from significant distance estimation errors.…”