A New Survey on Self-Tuning Integrated Low-Cost GPS/Ins Vehicle Navigation System in Harsh Environment

Navidi, Néda; Landry, René

doi:10.5194/isprsarchives-xl-1-w4-75-2015

Cited by 6 publications

(2 citation statements)

References 28 publications

(27 reference statements)

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“…The measurements were carried out in some parts of the Turin road network, with very different environmental conditions [20]: open-sky area (section A in Fig. 12), streets with high woods (section B) where the satellite visibility is reduced (visibility of 5 satellites as maximum), urban canyon (section C) and tunnel (section D) with a not negligible length (about 2 km).…”

Section: Kinematic Testmentioning

confidence: 99%

Comparison of two different mass-market IMU generations: Bias analyses and real time applications

Piras

Dabove

2016

2016 IEEE/ION Position, Location and Navigation Symposium (PLANS)

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The inertial positioning is becoming a technique gradually more and more used both in geomatics and in professional world, thanks to the improvement of the Inertial Measurement Unit (IMU) platforms performances combined to the reduction of the electronical element size and cost.A mass-market IMU platform has today costs of about few euros: this kind of instruments are installed in a widely instruments, typically used for integrated positioning such as smartphones, Mobile Mapping System (MMS) or Unmanned Aerial Vehicles (UAV), where the requested accuracy is lower.These kind of devices are traditionally called as "mass market", considering the level of distribution and the cost, but the quality and performance of the internal sensors could be completely different. In the case of low cost IMU is also possible to obtain completely different performance, even considering similar devices.In this work the performance of two IMU mass-market platforms have been analyzed, used in different positioning modes (static and kinematic), integrated also with GNSS single frequency receivers, analyzing the positioning accuracies obtainable in the presence or absence of obstacles that obstruct the satellite visibility. The comparison of these results will be carried out using a tactical grade platform, defined as a reference.Firstly some bias analyses are made, focusing the attention on errors estimation: the achievable accuracies in static mode will analyzed, estimating the noise through the classical tests available in the literature. These test were carried out in laboratory, with purpose to work in "free magnetic field" condition, considering that several IMUs contains an internal magnetometer which is disturbed by eventual artificial magnetic field.The Allan Variance of each sensors were estimated in order to identify eventually the flicker noise, white noise and random noise in the single devices. Each dataset (acceleration, angular velocity, magnetic field) has been also analyzed with wavelet filter, with purpose to remote the noise, using the filtered data for estimating the navigation solution.After that, the precision and accuracies obtainable in real conditions was investigated; these performance have been evaluated analyzing some kinematic trajectories and considering a low-cost Mobile Mapping system (MMS), which belongs to the Geomatics Group of the Politecnico di Torino.Accuracies of positions and attitude will be evaluated considering different methods of data processing in post processing approaches. Both the loosely coupled (LC) and tightly coupled (TC) approaches are followed and the results obtained with all two methods are shown and described. To do this, the Inertial Explorer 8.60 post processing software was used to determine positions and attitudes.In this study, an interesting comparison about two different mass-market IMU generations is realized, in particular for mobile mapping applications.

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Section: Kinematic Testmentioning

confidence: 99%

Comparison of two different mass-market IMU generations: Bias analyses and real time applications

Piras

Dabove

2016

2016 IEEE/ION Position, Location and Navigation Symposium (PLANS)

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“…GNSS/INS integration may suffer in some operating environments such as urban canyons, tunnels, and underground parking [ 7 ] where the GNSS signals are degraded or blocked for a long time [ 8 ], and the INS only provides the navigation solution, which is deteriorated after a short time because of drifts and biases of accelerometer and gyroscopes [ 9 ]. For example, Equation (1) shows the position error due to the INS biases.…”

Section: Introductionmentioning

confidence: 99%

Optical and Mass Flow Sensors for Aiding Vehicle Navigation in GNSS Denied Environment

Moussa

Zahran

Mostafa

et al. 2020

Sensors

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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.

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GNSS/MIMU tightly coupled integrated with improved multi-state ZUPT/DZUPT constraints for a Land vehicle in GNSS-denied enviroments

Ning

Sang

Yao

et al. 2020

International Journal of Image and Data Fusion

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A New Survey on Self-Tuning Integrated Low-Cost GPS/Ins Vehicle Navigation System in Harsh Environment

Cited by 6 publications

References 28 publications

Comparison of two different mass-market IMU generations: Bias analyses and real time applications

Comparison of two different mass-market IMU generations: Bias analyses and real time applications

Optical and Mass Flow Sensors for Aiding Vehicle Navigation in GNSS Denied Environment

GNSS/MIMU tightly coupled integrated with improved multi-state ZUPT/DZUPT constraints for a Land vehicle in GNSS-denied enviroments

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