Abstract

Nowadays, autonomous vehicles have achieved a lot of research interest regarding the navigation, the surrounding environmental perception, and control. Global Navigation Satellite System/Inertial Navigation System (GNSS/INS) is one of the significant components of any vehicle navigation system. However, GNSS has limitations in some operating scenarios such as urban regions and indoor environments where the GNSS signal suffers from multipath or outage. On the other hand, INS standalone navigation solution degrades over time due to the INS errors. Therefore, a modern vehicle navigation system depends on integration between different sensors to aid INS for mitigating its drift during GNSS signal outage. However, there are some challenges for the aiding sensors related to their high price, high computational costs, and environmental and weather effects. This paper proposes an integrated aiding navigation system for vehicles in an indoor environment (e.g., underground parking). This proposed system is based on optical flow and multiple mass flow sensors integrations to aid the low-cost INS by providing the navigation extended Kalman filter (EKF) with forward velocity and change of heading updates to enhance the vehicle navigation. The optical flow is computed for frames taken using a consumer portable device (CPD) camera mounted in the upward-looking direction to avoid moving objects in front of the camera and to exploit the typical features of the underground parking or tunnels such as ducts and pipes. On the other hand, the multiple mass flow sensors measurements are modeled to provide forward velocity information. Moreover, a mass flow differential odometry is proposed where the vehicle change of heading is estimated from the multiple mass flow sensors measurements. This integrated aiding system can be used for unmanned aerial vehicles (UAV) and land vehicle navigations. However, the experimental results are implemented for land vehicles through the integration of CPD with mass flow sensors to aid the navigation system.

Highlights

  • The Global Navigation Satellite System (GNSS) and Inertial Navigation System (INS) integration is the main integrated navigation component in land vehicles

  • The mass flow sensor measure flow rate from range −200 slm to +200 slm with operating temperature from −20 ◦ C to +80 ◦ C

  • The proposed system consists of an up-looking camera (CPD) and multiple mass flow sensors where the camera provides forward velocity information through Visual odometry (VO) while mass flow sensors provide the navigation filter with both velocity and change of heading updates

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Summary

Introduction

The Global Navigation Satellite System (GNSS) and Inertial Navigation System (INS) integration is the main integrated navigation component in land vehicles. Another research adopting vision aided inertial navigation system for UAV applications is shown in [40], their approach is based on optical flow and machine learning Gaussian process regression (GPR) to enhance the estimation of the vehicle velocity. They accommodate real-time incremental training session during GNSS availability, during the unavailability of GNSS the GPR attempt to correct the drift in both INS and VO systems. Searching for a new configuration for the typical aiding sensors (up-looking camera) and new aiding sensors (Mass Flow meter) to assist the low-cost INS during GNSS signal outage to improve the land vehicle navigation solution.

System Overview
Monocular Visual Odometry
Mass Flow Sensors
Mass Flow Velocity Estimation
Mass Flow Heading Change Estimation
Integration Scheme
Experimental Results
Velocity Estimation Results
Heading Change Estimation Results
Navigation States Estimation Results
Conclusions
Full Text
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