Augmented UAS navigation in GPS denied terrain environments using synthetic vision

Wang, Teng

doi:10.31274/etd-180810-5462

Cited by 4 publications

(9 citation statements)

References 43 publications

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“…A summary of the challenges of GNSS-Denied navigation and the research efforts intended to improve its performance are provided by [5,24,25]. Two promising techniques for completely eliminating the position drift are the use of signals of opportunity 23 [26, 27, 28], and georegistration 24 [29,30,31,32], also known as image registration.…”

Section: A Gnss-denied Navigation and Visual Inertial Odometrymentioning

confidence: 99%

Reduction of GNSS-Denied inertial navigation errors for fixed wing autonomous unmanned air vehicles

Gallo

Barrientos

2022

Aerospace Science and Technology

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Section: A Gnss-denied Navigation and Visual Inertial Odometrymentioning

confidence: 99%

Reduction of GNSS-Denied inertial navigation errors for fixed wing autonomous unmanned air vehicles

Gallo

Barrientos

2022

Aerospace Science and Technology

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“…Georegistration matches landmarks or terrain features as scanned or imaged by vehicles to preloaded data [7], and can work based on Synthetic Aperture Radar (SAR) [18,19,20], Light Detection and Ranging (LIDAR) [21,22,23], or camera visual systems, in what is known as image registration [24,25,26,27]. While SAR suffers from being memory and computationally expensive and LIDAR is restricted for aviation purposes by its limited range, image registration is a potentially valid solution for completely removing the position drift in GNSS-Denied scenarios.…”

Section: Objectives and Noveltymentioning

confidence: 99%

“…When the GNSS signals are lost, the filter does not have enough observations to properly estimate ∆p, so it keeps the estimation constant per (27). This process thus incorporates the pressure offset variation between the time at which the geometric altitude is estimated and the time at which the GNSS signals are lost as the fourth primary error source.…”

Section: Vertical Position Estimationmentioning

confidence: 99%

“…The position filter does not integrate the vertical speed to obtain the vertical position or geometric altitude, but instead estimates it through (31) and (32) from the pressure altitude ĤP and temperature offset ∆ T estimated by the air data filter, together with the pressure offset ∆p estimated at the time the GNSS signals are lost (27). This approach leaves the vertical position estimation susceptible to atmospheric pressure changes, and results in an estimation error that is bounded by atmospheric physics, but proportional to the amount of pressure offset ∆p change from t GNSS to the time the vertical position is being estimated.…”

Section: Different Vertical Position Algorithmsmentioning

confidence: 99%

“…Only the scenario #1 results are shown, as the behavior of the air data filter is similar on both scenarios. Influence of airspeed sensor quality on aggregated airspeed vector NSE for scenario #1 (100 runs)The influence of the airspeed sensors quality on the body attitude for both scenarios is summarized in table 21, together with figures 27 and 2827 . There is a clear correlation between the sensors quality and the aggregated body attitude NSE, which is caused by the accuracy of the attitude filter observations, in particular those of p n(14).…”

mentioning

confidence: 99%

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Minimization of GNSS-Denied Inertial Navigation Errors for Fixed Wing Autonomous Unmanned Air Vehicles

Gallo,

Barrientos

2021

Preprint

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This article proposes an inertial navigation algorithm intended to lower the negative consequences of the absence of GNSS (Global Navigation Satellite System) signals on the navigation of autonomous fixed wing low SWaP (Size, Weight, and Power) UAVs (Unmanned Air Vehicles). In addition to accelerometers and gyroscopes, the filter takes advantage of sensors usually present onboard these platforms, such as magnetometers, Pitot tube, and air vanes, and aims to minimize the attitude error and reduce the position drift (both horizontal and vertical) with the dual objective of improving the aircraft GNSS-Denied inertial navigation capabilities as well as facilitating the fusion of the inertial filter with visual odometry algorithms. Stochastic high fidelity Monte Carlo simulations of two representative scenarios involving the loss of GNSS signals are employed to evaluate the results, compare the proposed filter with more traditional implementations, and analyze the sensitivity of the results to the quality of the onboard sensors. The author releases the C ++ implementation of both the navigation filter and the high fidelity simulation as open-source software [1].

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GNSS-Denied Semi-Direct Visual Navigation for Autonomous UAVs Aided by PI-Inspired Inertial Priors

Gallo

Barrientos

2023

Aerospace

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This article proposes a method to diminish the horizontal position drift in the absence of GNSS (Global Navigation Satellite System) signals experienced by the VNS (Visual Navigation System) installed onboard a UAV (Unmanned Air Vehicle) by supplementing its pose estimation non-linear optimizations with priors based on the outputs of the INS (Inertial Navigation System). The method is inspired by a PI (Proportional Integral) control loop, in which the attitude and altitude inertial outputs act as targets to ensure that the visual estimations do not deviate past certain thresholds from their inertial counterparts. The resulting IA-VNS (Inertially Assisted Visual Navigation System) achieves major reductions in the horizontal position drift inherent to the GNSS-Denied navigation of autonomous UAVs. Stochastic high-fidelity Monte Carlo simulations of two representative scenarios involving the loss of GNSS signals are employed to evaluate the results and to analyze their sensitivity to the terrain type overflown by the aircraft. The authors release the C++ implementation of both the navigation algorithms and the high-fidelity simulation as open-source software.

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Augmented UAS navigation in GPS denied terrain environments using synthetic vision

Cited by 4 publications

References 43 publications

Reduction of GNSS-Denied inertial navigation errors for fixed wing autonomous unmanned air vehicles

Reduction of GNSS-Denied inertial navigation errors for fixed wing autonomous unmanned air vehicles

Minimization of GNSS-Denied Inertial Navigation Errors for Fixed Wing Autonomous Unmanned Air Vehicles

GNSS-Denied Semi-Direct Visual Navigation for Autonomous UAVs Aided by PI-Inspired Inertial Priors

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