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.
Highlights
GPS (Global Positioning System) revolutionized the way we navigate outdoors as it is used by pedestrians, cars, planes, and military vehicles to reach certain goals
The usage of sequences allows our visual localization subsystem to operate in a highly self-similar environment, while further modifications to the FastABLE algorithm proposed in this paper make it possible to obtain an accurate position estimate of the user with respect to a known map of images
The proposed procedure, inspired by the Weighted K Nearest Neighbors (WKNN) algorithm used to process the matching WiFi scans, yields metric position information which is introduced as constraints to the graph-based localization framework
Summary
GPS (Global Positioning System) revolutionized the way we navigate outdoors as it is used by pedestrians, cars, planes, and military vehicles to reach certain goals. The recent changes in Android WiFi scanning policy (https://developer.android.com/guide/topics/connectivity/wifi-scan) will require modifications to those approaches since the WiFi scanning rate is reduced to four scans per two minutes in the latest Android devices This is a significantly lower value than the scanning frequency typically used to obtain a reasonably dense positioning with WiFi-based fingerprinting [4]. The concept of localization constraints stemming from qualitative observations of the environment or information entered directly by the user was introduced in our conference paper [6] This journal article significantly extends the preliminary approach from [6], building upon our more recent graph-based positioning system utilizing WiFi fingerprinting [4]. We show that our new approach meets the needs arising from the recent changes in Android security policies
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