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Tightly-Coupled VLP/INS Integrated Navigation by Inclination Estimation and Blockage Handling

Xiao Sun, Yuan Zhuang, Xiansheng Yang, Jianzhu Huai, Tianming Huang, Daquan Feng

TL;DR

This work tackles indoor localization challenges by tightly coupling Visible Light Positioning (VLP) with an Inertial Navigation System (INS) to jointly estimate position and PD inclination while handling LOS blockages. The authors develop a quaternion-based, Lambertian RSS model, derive disturbance-aware Jacobians, and implement a blockage-detection mechanism (DRD) to down-weight blocked observations within a graph-optimization framework over a sliding window. Key contributions include: (i) a 6DoF tightly-coupled VLP/INS architecture with attitude observability analysis, (ii) a blockage-detection and exclusion strategy for robust RSS measurements, and (iii) the feasibility of estimating unknown LED positions within the same optimization. Experimental validation across simulations and real-world tests shows ~10 cm positioning accuracy and ~1° inclination accuracy under movement, inclinations, and blockages, with large-scale tests confirming robustness and competitiveness against UWB and BLE baselines.

Abstract

Visible Light Positioning (VLP) has emerged as a promising technology capable of delivering indoor localization with high accuracy. In VLP systems that use Photodiodes (PDs) as light receivers, the Received Signal Strength (RSS) is affected by the incidence angle of light, making the inclination of PDs a critical parameter in the positioning model. Currently, most studies assume the inclination to be constant, limiting the applications and positioning accuracy. Additionally, light blockages may severely interfere with the RSS measurements but the literature has not explored blockage detection in real-world experiments. To address these problems, we propose a tightly coupled VLP/INS (Inertial Navigation System) integrated navigation system that uses graph optimization to account for varying PD inclinations and VLP blockages. We also discussed the possibility of simultaneously estimating the robot's pose and the locations of some unknown LEDs. Simulations and two groups of real-world experiments demonstrate the efficiency of our approach, achieving an average positioning accuracy of 10 cm during movement and inclination accuracy within 1 degree despite inclination changes and blockages.

Tightly-Coupled VLP/INS Integrated Navigation by Inclination Estimation and Blockage Handling

TL;DR

This work tackles indoor localization challenges by tightly coupling Visible Light Positioning (VLP) with an Inertial Navigation System (INS) to jointly estimate position and PD inclination while handling LOS blockages. The authors develop a quaternion-based, Lambertian RSS model, derive disturbance-aware Jacobians, and implement a blockage-detection mechanism (DRD) to down-weight blocked observations within a graph-optimization framework over a sliding window. Key contributions include: (i) a 6DoF tightly-coupled VLP/INS architecture with attitude observability analysis, (ii) a blockage-detection and exclusion strategy for robust RSS measurements, and (iii) the feasibility of estimating unknown LED positions within the same optimization. Experimental validation across simulations and real-world tests shows ~10 cm positioning accuracy and ~1° inclination accuracy under movement, inclinations, and blockages, with large-scale tests confirming robustness and competitiveness against UWB and BLE baselines.

Abstract

Visible Light Positioning (VLP) has emerged as a promising technology capable of delivering indoor localization with high accuracy. In VLP systems that use Photodiodes (PDs) as light receivers, the Received Signal Strength (RSS) is affected by the incidence angle of light, making the inclination of PDs a critical parameter in the positioning model. Currently, most studies assume the inclination to be constant, limiting the applications and positioning accuracy. Additionally, light blockages may severely interfere with the RSS measurements but the literature has not explored blockage detection in real-world experiments. To address these problems, we propose a tightly coupled VLP/INS (Inertial Navigation System) integrated navigation system that uses graph optimization to account for varying PD inclinations and VLP blockages. We also discussed the possibility of simultaneously estimating the robot's pose and the locations of some unknown LEDs. Simulations and two groups of real-world experiments demonstrate the efficiency of our approach, achieving an average positioning accuracy of 10 cm during movement and inclination accuracy within 1 degree despite inclination changes and blockages.
Paper Structure (21 sections, 30 equations, 17 figures, 5 tables, 1 algorithm)

This paper contains 21 sections, 30 equations, 17 figures, 5 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. System Overview
  3. RSS-based Indoor Visible Light Positioning
  4. Quaternion-based Lambertian Model
  5. Disturbance Model and Observability
  6. Blockage Detection
  7. Tightly-coupled VLP/INS Positioning
  8. IMU Pre-Integration and Residuals
  9. Graph Optimization for VLP/INS Integrated Navigation
  10. Unknown LED parameters
  11. Experiment Setup
  12. Results and Analysis
  13. Simulation
  14. Normal Tests without Inclination and Blockage
  15. Tests with Inclination
  16. ...and 6 more sections

Figures (17)

  • Figure 1: A flow chart showing the difference between tightly-coupled and loosely-coupled VLP/INS integration.
  • Figure 2: The overall architecture of the proposed tightly-coupled VLP/INS integrated navigation system.
  • Figure 3: The coordinate systems defined in our system.
  • Figure 4: Illustration of the blockage for a sinusoidal-modulated wave and the influence on its DFT transformation.
  • Figure 5: The blockage detection process by the value of RSS changing rate ratio.
  • ...and 12 more figures