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Applying a ToF/IMU-Based Multi-Sensor Fusion Architecture in Pedestrian Indoor Navigation Methods.

Farzan Farhangian¹, Mohammad Sefidgar¹, Rene Jr Landry¹

¹Laboratory of Space Technologies, Embedded Systems, Navigation and Avionic (LASSENA), Department of Electrical Engineering, École de Technologie Supérieure, Montreal, QC H3C 1K3, Canada.

Sensors (Basel, Switzerland)

|June 2, 2021

Summary

This study introduces a new pedestrian indoor navigation system using Time of Flight sensors and dual Inertial Measurement Units (IMUs). The enhanced system improves reliability and accuracy for indoor localization, reducing errors in challenging environments.

Keywords:

IMU inertial navigation foot-mounted INS indoor navigation online calibration pedestrian navigation time of flight sensor

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Area of Science:

Robotics
Navigation Systems
Sensor Fusion

Background:

Low-cost Inertial Measurement Units (IMUs) in indoor Inertial Navigation Systems (INS) suffer from unreliability and inaccuracies.
Sources of error include imperfect modeling, sensor fusion, heading drift, IMU biases, and calibration issues.

Purpose of the Study:

To address the unreliability of Micro-Electro-Mechanical System (MEMS)-based pedestrian INS.
To develop a novel multi-sensor fusion method for improved indoor pedestrian localization.

Main Methods:

A novel fusion method using a Time of Flight (ToF) sensor and dual chest/foot-mounted IMUs with online calibration.
An Extended Kalman Filter (EKF) for estimating attitude, position, velocity errors, and IMU biases.
A fusion architecture integrating ToF and foot-mounted IMU for velocity, with chest-mounted IMU for attitude and a corridor detection filter for heading drift.

Main Results:

The developed system demonstrated promising and resilient results in 2D corridor spaces for up to 11 minutes.
Achieved a position Root Mean Square (RMS) error of less than 3 meters.
Attained a final-point error of less than 5 meters.

Conclusions:

The novel multi-sensor fusion approach significantly enhances the reliability and accuracy of low-cost MEMS-based pedestrian INS.
The system effectively mitigates common error sources, offering robust performance in indoor environments like corridors.