Video 1_Further elaboration on acoustic-assisted indoor pedestrian dead reckoning—The extended implementation details and new evaluations.mp4

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>