Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
112 tokens/sec
GPT-4o
8 tokens/sec
Gemini 2.5 Pro Pro
47 tokens/sec
o3 Pro
5 tokens/sec
GPT-4.1 Pro
38 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

PEOPLEx: PEdestrian Opportunistic Positioning LEveraging IMU, UWB, BLE and WiFi (2311.18182v1)

Published 30 Nov 2023 in cs.RO

Abstract: This paper advances the field of pedestrian localization by introducing a unifying framework for opportunistic positioning based on nonlinear factor graph optimization. While many existing approaches assume constant availability of one or multiple sensing signals, our methodology employs IMU-based pedestrian inertial navigation as the backbone for sensor fusion, opportunistically integrating Ultra-Wideband (UWB), Bluetooth Low Energy (BLE), and WiFi signals when they are available in the environment. The proposed PEOPLEx framework is designed to incorporate sensing data as it becomes available, operating without any prior knowledge about the environment (e.g. anchor locations, radio frequency maps, etc.). Our contributions are twofold: 1) we introduce an opportunistic multi-sensor and real-time pedestrian positioning framework fusing the available sensor measurements; 2) we develop novel factors for adaptive scaling and coarse loop closures, significantly improving the precision of indoor positioning. Experimental validation confirms that our approach achieves accurate localization estimates in real indoor scenarios using commercial smartphones.

Citations (1)

Summary

  • The paper introduces an opportunistic multi-sensor framework that fuses IMU, UWB, BLE, and WiFi signals without relying on prior environmental data.
  • The paper employs adaptive scaling and fingerprint-based loop closures to correct drift and compensate for variable step lengths or velocities.
  • The paper validates its approach through experiments with commercial devices, showing enhanced positioning accuracy even with minimal UWB anchor usage.

PEOPLEx: PEdestrian Opportunistic Positioning Leveraging IMU, UWB, BLE and WiFi

PEOPLEx represents a progressive attempt to address challenges in indoor pedestrian localization using an opportunistic multi-sensor framework that integrates IMU-based inertial navigation as the primary method, supplemented by UWB, BLE, and WiFi signals. This approach underscores the importance of utilizing available sensors dynamically without requiring prior environmental knowledge. Below, we delve into the core contributions, methodology, and implications of this research within the broader context of indoor positioning systems (IPS).

Contributions and Methodology

The primary contributions of the PEOPLEx framework lie in its novel approach to multi-sensor integration and the development of custom factors for enhancing positioning accuracy:

  1. Opportunistic Multi-Sensor Integration:
    • The framework relies on pedestrian inertial navigation (IMU data) as the backbone and opportunistically integrates data from UWB, BLE, and WiFi when available.
    • A nonlinear factor graph optimization technique fuses sensor inputs, mitigating the need for prior environmental information such as anchor positions or radio frequency maps.
  2. Adaptive Scaling and Coarse Loop Closures:
    • Adaptive Scaling: Custom factors are introduced to dynamically estimate step lengths (PDR method) or velocity scaling (RoNIN method), addressing challenges posed by individual variations and drifts in pedestrian trajectories.
    • Coarse Loop Closures: BLE and WiFi fingerprinting are used to establish loop closures between poses with high similarity in fingerprints. This technique allows for robust correction of drift in inertial navigation estimates.

Experimental Validation

Experimental validation was conducted using commercial smartphones and UWB devices in real indoor environments. The experiments highlight several key findings:

  • Impact of UWB Anchors:
    • Accuracy improves with the number of UWB anchors, with substantial gains observed even with a single anchor. Adaptive scaling approaches (Adaptive PDR and Adaptive RoNIN) showcased significant improvements over fixed scaling methods.
  • Initial Scale Value Sensitivity:
    • Adaptive methods effectively corrected initial erroneous scale values, demonstrating robustness in varying initial conditions.
  • Robustness to Anchor Positioning Noise:
    • The system's ability to estimate anchor positions as part of the optimization process ensures accuracy even with initial placement errors, differentiating it from conventional range-only methods that require accurate anchor positioning.

Theoretical and Practical Implications

The theoretical implications of PEOPLEx extend to adaptive methodologies within the domain of sensor fusion for IPS. The use of nonlinear factor graph optimization provides a flexible framework that can be adapted to various multi-sensor setups and environmental conditions. By not relying on predefined environmental knowledge, PEOPLEx offers a scalable and cost-effective solution for real-time pedestrian localization.

In practice, PEOPLEx offers significant improvements in applications requiring robust indoor localization, including:

  • Navigation Assistance: Enhanced accuracy in tracking pedestrian movements can provide more reliable navigation aids in complex indoor environments like shopping malls and airports.
  • Location-Based Services: Improved positioning accuracy allows for more precise delivery of location-based content and services, enhancing user experience.
  • Safety and Security: Real-time, reliable localization can contribute to better safety measures in industrial settings, enabling more precise monitoring and control of pedestrian movements.

Future Developments

Looking forward, there are several avenues for enhancing the PEOPLEx framework:

  • Activity-Aware Localization: Integrating context-aware algorithms that adjust localization strategies based on user activities can further refine accuracy.
  • On-Device Estimation: Advances in mobile processing capabilities may allow for more computations to be performed directly on the device, reducing reliance on central servers and enhancing real-time performance.
  • Integration of Additional Sensors: Exploring additional sensing modalities such as WiFi RTT or CSI can provide richer datasets for localization, potentially increasing accuracy and robustness further.

In summary, the PEOPLEx framework presents a comprehensive, adaptable solution for pedestrian localization utilizing existing sensor capabilities in a dynamic, opportunistic manner. Its contributions to adaptive scaling, robust sensor fusion, and drift correction mark notable advancements in the field of indoor positioning systems.