Robust Indoor Pedestrian Backtracking Using Magnetic Signatures and Inertial Data.

Chia Hsuan Tsai, Roberto Manduchi
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Abstract

Navigating unfamiliar environments can be challenging for visually impaired individuals due to difficulties in recognizing distant landmarks or visual cues. This work focuses on a particular form of wayfinding, specifically backtracking a previously taken path, which can be useful for blind pedestrians. We propose a hands-free indoor navigation solution using a smartphone without relying on pre-existing maps or external infrastructure. Our hybrid matching method integrates machine learning to enhance positioning accuracy, addressing real-life challenges such as odometry errors or deviations from the correct path. Testing with datasets from visually impaired individuals demonstrates the potential of our approach in providing reliable backtracking assistance.

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PALMS: Plane-based Accessible Indoor Localization Using Mobile Smartphones. Robust Indoor Pedestrian Backtracking Using Magnetic Signatures and Inertial Data. Step Length Is a More Reliable Measurement Than Walking Speed for Pedestrian Dead-Reckoning.
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