Robust Indoor Pedestrian Backtracking Using Magnetic Signatures and Inertial Data

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.