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arXiv · 2610.39691

Smartphone Indoor Localization Enhanced by Hierarchical Multi-Sensor Context Recognition

Wenhui Feng·Muhan Weng·Jianyu Liu·Xiansheng Yang·Hao Zhang·Liang Wang·Yuan Zhuang
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Abstract

Smartphone indoor localization requires low-cost, robust positioning across scene transitions and phone-use variations. Pedestrian dead reckoning (PDR) enables infrastructure-free deployment but accumulates errors from heading drift, structural transitions, and carrying-mode changes. Existing methods often treat contextual cues as a single event stream or local correction signal, limiting their ability to address distinct localization errors. This paper presents a framework enhanced by hierarchical multi-sensor context recognition. A jointly data- and model-driven recognition model predicts environment, scene, and activity probabilities, which are converted into scene-event and action-context segments. Scene-event segments guide scene-constrained PDR correction through corridor heading constraints, landmark attraction, fixed-lag backtracking, and vertical-transition handling. Action-context segments drive action-aware RoNIN-based position compensation for carrying-mode-dependent drift. Experiments on cross-floor and multi-action trajectories against LiDAR-SLAM references show that the proposed framework outperforms PDR and representative learned inertial odometry baselines, demonstrating that hierarchical semantics provide reliable, localization-specific correction cues.

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Wenhui Feng, Muhan Weng, Jianyu Liu, Xiansheng Yang, Hao Zhang, Liang Wang, Yuan Zhuang. 2026-10-29. Smartphone Indoor Localization Enhanced by Hierarchical Multi-Sensor Context Recognition. https://arxiv.org/abs/2610.39691

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