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

Farzan Farhangian,Mohammad Sefidgar,Rene Jr. Landry

doi:10.3390/s21113615

Farzan Farhangian, Mohammad Sefidgar + Show 1 more

Open Access

https://doi.org/10.3390/s21113615

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Abstract

The advancement of indoor Inertial Navigation Systems (INS) based on the low-cost Inertial Measurement Units (IMU) has been long reviewed in the field of pedestrian localization. There are various sources of error in these systems which lead to unstable and unreliable positioning results, especially in long term performances. These inaccuracies are usually caused by imperfect system modeling, inappropriate sensor fusion models, heading drift, biases of IMUs, and calibration methods. This article addresses the issues surrounding unreliability of the low-cost Micro-Electro-Mechanical System (MEMS)-based pedestrian INS. We designed a novel multi-sensor fusion method based on a Time of Flight (ToF) distance sensor and dual chest- and foot-mounted IMUs, aided by an online calibration technique. An Extended Kalman Filter (EKF) is accounted for estimating the attitude, position, and velocity errors, as well as estimation of IMU biases. A fusion architecture is derived to provide a consistent velocity measurement by operative contribution of ToF distance sensor and foot mounted IMU. In this method, the measurements of the ToF distance sensor are used for the time-steps in which the Zero Velocity Update (ZUPT) measurements are not active. In parallel, the chest mounted IMU is accounted for attitude estimation of the pedestrian’s chest. As well, by designing a novel corridor detection filter, the heading drift is restricted in each straightway. Compared to the common INS method, developed system proves promising and resilient results in two-dimensional corridor spaces for durations of up to 11 min. Finally, the results of our experiments showed the position RMS error of less than 3 m and final-point error of less than 5 m.

Highlights

Nowadays, indoor localization systems are being used in numerous applications, especially in situations in which the Global Positioning System (GPS) or other signals may have less coverage
We have presented an innovative multi-sensor fusion approach for Time of Flight (ToF) sensor and dual Inertial Measurement Units (IMU) sensors mounted on the chest and the foot
The 9-degree of freedom (DoF) IMU installed on the chest provides an accurate attitude and heading estimation

Summary

Introduction

Indoor localization systems are being used in numerous applications, especially in situations in which the Global Positioning System (GPS) or other signals may have less coverage. Some indoor positioning systems have been usually based on inertial sensors, in recent decades various sensor fusion methods could increase the accuracy of these systems. In order to satisfy the positioning accuracy of a mobile pedestrian, an adaptive and online calibration method is needed to increase the accuracy of traditional footmounted INS algorithms. An online calibration is defined as a process of error estimation for pedestrian’s position, velocity, and attitude, followed by updating the system states in each time-step. A novel sensor fusion architecture can lead the system to have more accurate error estimation in each EKF or other KF-based models. The architecture of the presented model is based on dual IMUs and a time of flight (ToF) distance sensor. In contrast to the other usual foot-mounted INS methodologies, this method uses a chest-mounted IMU to correct the orientation and heading estimation

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Discussion

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