Organizations: Wenhui Feng, Muhan Weng, and Jianyu Liu are with the Electronic Information School, Wuhan University, Wuhan, Hubei 430072, China · Xiansheng Yang, Hao Zhang, and Yuan Zhuang are with the State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing (LIESMARS), Wuhan University, Wuhan, Hubei 430072, China · Liang Wang is with the Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
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.
Figures & tables
Fig. 1: Overview of the key challenges and the proposed smartphone localization framework enhanced by hierarchical multi-sensor context recognition.
Method family
Context cue and use
Our Work
Context-aware PDR
Motion state, carrying mode, or map structure adjusts steps, headings, and floor transitions.
Scene events drive map-guided PDR correction.
Semantic-aided localization
Scene, activity, or landmark events constrain routes, topology, or graph factors.
Environment, scene, and activity cues are separated before localization.
Learned inertial odometry
IMU windows predict velocity or displacement in a learned trajectory stream.
TABLE I: Context Cues in Related Localization Methods
Fig. 2: Hierarchical multi-sensor context recognition model. The model extracts temporal and mask-aware statistical features for each sensor, aggregates cross-sensor relations with a fully connected graph-attention module, and decodes environment, scene, and activity probabilities with top-down semantic constraints.
Fig. 3: Auxiliary localization framework driven by recognition-derived semantic cues. Scene-event segments activate scene-constrained auxiliary localization for PDR, while action-context segments guide action-aware RoNIN-based learned position compensation.
Sensor
Recorded attributes
Sampling rate
Accelerometer
3-axis linear acceleration
100–200 Hz
Gyroscope
3-axis angular velocity
100–200 Hz
Magnetometer
3-axis magnetic field
100 Hz
AHRS
Pitch, roll, yaw, quaternion
100–200 Hz
Barometer
Pressure
20 Hz
Light
Illuminance
5–10 Hz
TABLE II: Smartphone Sensor Configuration Used in the Experiments
Parameter
Value
Semantic and elevator confidence threshold, τq
0.9
Minimum transition-event persistence, Nmin
2 consecutive windows
Corridor pull strength, λ
0.6
Corridor heading tolerance, ε
20∘
Maximum position correction, dmax
20.0 m
Correction regularization coefficient, β
0.005 (training)
TABLE III: Principal Parameters Used in the Localization Experiments
Model
Environment Accuracy
Scene Accuracy
Activity Accuracy
Joint Accuracy
Mean Macro-F1
Ours
97.54
95.24
98.06
94.35
96.83
TABLE IV: Recognition Performance of the Semantic Front End (%)
Method
Description
PDR
Step-wise dead reckoning baseline driven by pedestrian motion estimation and heading updates.
RoNIN
Robust neural inertial navigation baseline used as the main learned trajectory reference.
MEIO
Motion-aware inertial odometry baseline with adaptive computation design.
RIDI
Double-integration-based inertial baseline representing a classic learning-based IMU pipeline.
TLIO
Tight learned inertial odometry baseline for drift-aware trajectory estimation.
Our Work
Hierarchical semantic interface with scene-constrained PDR correction on Route A and action-aware RoNIN position compensation on Route B.
TABLE V: Compared Methods Used in the Experiments
Method
ATE (m)
RTE@10 m (m)
APE med (m)
Route A
PDR
17.705
12.298
7.974
TLIO
8.666
6.164
5.214
MEIO
5.537
5.293
7.593
RIDI
7.280
6.933
4.307
RoNIN
3.727
4.738
1.945
Our Work
2.699
3.765
1.524
TABLE VI: Overall Quantitative Comparison on Route A and Route B
Fig. 4: Route A trajectory comparison across floor transitions. (a) Initial trajectory segment on the second floor. (b) Trajectory segment after descending to the first floor. (c) Trajectory segment after returning to the second floor.
Fig. 5: Route B overall trajectory comparison under multiple phone-use switches.
Fig. 6: Overall error analysis. The upper row shows displacement-error CDFs, and the lower row shows APE time series. (a) Route A displacement-error CDF. (b) Route B displacement-error CDF. (c) Route A APE time-series comparison. (d) Route B APE time-series comparison.
Fig. 7: Localized gain analysis around semantic triggers. The upper row shows APE curves in [−15,25] s windows, and the lower row shows APE distributions. (a) Route A key-point APE evolution. (b) Route B action-switch APE evolution. (c) Route A key-point APE distribution. (d) Route B action-switch APE distribution.
TABLE VII: Ablation results for Route A scene-constrained components and Route B action-aware components.
Modern pedestrian dead reckoning (PDR) systems rely on fusing noisy and biased estimates of position, velocity, and calibrated orientation derived from loosely coupled sensors to determine the current pose of a localized object. However, discrepancies in the sampling rates of sensor-specific estimation methods and unreliable transmission pose significant challenges. And traditional methods often fail to effectively fuse multimodal sensor data during dynamic movements characterized by high accelerations, velocities, and rapidly varying orientations. To address these limitations, we propose a simple recurrent neural network (RNN) architecture capable of implicitly forecasting asynchronous sensor data streams from diverse estimation methods along reference trajectories. The proposed approach introduces PDRNN, a modular hybrid AI-assisted PDR system that handles each component as an independent ensemble of machine learning (ML) models to estimate both key parameter means and variances. Separate ML-based models are employed to estimate orientation, (un)directed velocity or distance from acceleration and gyroscope data, with optional absolute positioning from synchronized radio systems such as 5G for stabilization. A final fusion model combines these outputs, position, velocity, and orientation, while using uncertainty estimates to enhance system robustness. The modular design allows individual components to be updated, fine-tuned, or replaced without affecting the entire system. Experiments on dynamic sports movement data show that PDRNN achieves superior accuracy and precision compared to classic and ML-based methods, effectively avoiding error accumulation common in black-box approaches. And PDRNN offers forecast capabilities and better component control despite increased system complexity.
Peter Bauer, Andreas Porada, Felix Ott +2
Fraunhofer Institute for Integrated Circuits IIS, 90411 Nürnberg, Germany · Friedrich-Alexander-Universität Erlangen-Nürnberg, 91058 Erlangen, Germany
Ultra-wideband (UWB) indoor localization provides centimeter-level accuracy and low latency, but its measurement reliability degrades severely under Non-Line-of-Sight (NLOS) conditions, leading to meter-scale ranging errors and inconsistent uncertainty characteristics. Inertial Measurement Unit (IMU)-based Pedestrian Dead Reckoning (PDR) complements UWB by providing infrastructure-free motion estimation; however, its error accumulates nonlinearly over time due to bias and noise propagation. Fusion methods based on Extended Kalman Filters (EKF) and Particle Filters (PF) can improve average localization accuracy through probabilistic state estimation. However, these approaches typically rely on manually tuned measurement covariances. Such fixed or heuristically tuned parameters are hard to sustain across varying indoor layouts, NLOS ratios, and motion patterns, leading to limited robustness and poor generalization of measurement uncertainty modeling in heterogeneous environments. To address this limitation, this work proposes an adaptive measurement covariance scaling framework in which reliability cues are learned from historical UWB/PDR trajectories. A large teacher model is employed offline to generate temporally consistent next-position predictions from structured UWB/PDR sequences, and this behavior is distilled into a lightweight student model suitable for real-time deployment. The student model continuously regulates EKF measurement covariances based on prediction residuals, enabling environment-aware fusion without manual re-tuning. Experimental results demonstrate that the proposed KD-EKF framework significantly reduces localization error, suppresses error spikes during Line-of-Sight (LOS)/NLOS transitions, and mitigates long-term drift compared to fixed-parameter EKF, thereby improving measurement robustness across diverse indoor environments.
Accurate localization is critical for a wide range of applications, including navigation, augmented reality, safety management, and healthcare. However, achieving accurate positioning remains challenging, especially in GPS-degraded environments. Pedestrian Dead Reckoning (PDR), which estimates position based on inertial sensor data, offers a promising alternative but typically demands substantial effort in algorithm design and system integration. This complexity often leads to prolonged development timelines and elevated costs. To address these challenges, we present the PDR Sensor (PDRS), a plug-and-play, black-box PDR module optimized for seamless integration into wearable and IoT platforms. PDRS integrates a microprocessor and an inertial measurement unit (IMU) within a compact, low-power module. It executes real-time sensor fusion onboard, providing position updates via standard interfaces (I2C, SPI, UART). Experimental evaluations demonstrate its high robustness, achieving a step detection error of just 0.16% over 1250 steps and a traveled distance error of 1.87% under diverse conditions. The system further exhibits low latency (2.5 ms) and modest power consumption (52.35 mA). Unlike conventional studies, the PDRS leverages a holistic co-design of hardware and software to enhance performance and integration aspects, simplifying adoption without requiring deep expertise. By lowering technical barriers, PDRS enables developers and researchers to prioritize innovation in IoT and wearable technologies, accelerating prototyping, reducing development time, and advancing industry progress. Future work aims to enhance accuracy, reduce power usage and module size, and integrate AI-driven techniques to better mitigate drift.