2026-10-12T10:03:48Z https://api.figshare.com/v2/oai

oai:figshare.com:article/33717628 2026-09-14T04:27:34Z category_76 portal_316 item_type_2 month_year_09_2026

Video 1_Further elaboration on acoustic-assisted indoor pedestrian dead reckoning—The extended implementation details and new evaluations.mp4 Yang Wang (5921) Takuya Maekawa (6892613) Theoretical Computer Science acoustic sensing indoor positioning offline trajectory reconstruction pedestrian tracking range Doppler <p>Indoor pedestrian dead reckoning (PDR) using smartphone inertial sensors suffers from accumulated errors, so researchers use various indoor landmarks for correction. This study proposes acoustic sensing—using a smartphone's speaker and microphone to detect doors and walls—as a new ubiquitous landmark requiring no extra hardware or environment-specific training data. We present a neural-network-based acoustic event detector for door-passing and wall-proximity events with wall-distance estimation, and a particle-filter trajectory algorithm that uses these landmarks and map matching to correct inertial drift for offline trajectory reconstruction. Compared to the conference version, this paper adds implementation details, further experiments, and a thorough discussion of the method's limitations. Consistent with the conference version, results show that under relatively ideal conditions and a strict grasping posture, the method substantially outperforms the NeuralPDR baseline. However, the expanded evaluation shows that landmark recognition degrades sharply when grasping posture changes or obstacles/noise are present, that trajectory error grows exponentially at low landmark recall, and that the method's real-time processing, power, and memory demands remain impractical.</p> 2026-09-14T04:27:34Z Dataset Media 10.3389/fcomp.2026.1834280.s001 https://figshare.com/articles/media/Video_1_Further_elaboration_on_acoustic-assisted_indoor_pedestrian_dead_reckoning_The_extended_implementation_details_and_new_evaluations_mp4/33717628 CC BY 4.0