A Model Aided Inertial Navigation System for Automatic Landing of Unmanned Aerial Vehicles

Navigation ◽  
2018 ◽  
Vol 65 (2) ◽  
pp. 183-204 ◽  
Author(s):  
Hamed Mohammadkarimi ◽  
Hadi Nobahari
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Navigation System ◽  
Inertial Navigation System ◽  
Inertial Navigation ◽  
Aerial Vehicles ◽  
Automatic Landing

scholarly journals The research of inertial navigation system systematic errors at aerial photography from unmanned aerial vehicles

ScienceRise ◽  
2015 ◽  
Vol 9 (2(14)) ◽  
pp. 6
Author(s):  
Роман Володимирович Шульц ◽  
Петр Давидович Крельштейн ◽  
Ірина Анатоліївна Маліна
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Aerial Photography ◽  
Navigation System ◽  
Inertial Navigation System ◽  
Inertial Navigation ◽  
Systematic Errors ◽  
Aerial Vehicles

Vision-aided terrain referenced navigation for unmanned aerial vehicles using ground features

2014 ◽  
Vol 228 (13) ◽  
pp. 2399-2413 ◽  
Author(s):  
Dongjin Lee ◽  
Youngjoo Kim ◽  
Hyochoong Bang
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Navigation System ◽  
Autonomous Navigation ◽  
Low Cost ◽  
Translational Motion ◽  
Inertial Navigation ◽  
Navigation Data ◽  
Terrain Referenced Navigation ◽  
Error Covariance ◽  
Aerial Vehicles

A vision-aided terrain referenced navigation (VATRN) approach is addressed for autonomous navigation of unmanned aerial vehicles (UAVs) under GPS-denied conditions. A typical terrain referenced navigation (TRN) algorithm blends inertial navigation data with measured terrain information to estimate vehicle’s position. In this paper, a low-cost inertial navigation system (INS) for UAVs is supplemented with a monocular vision-aided navigation system and terrain height measurements. A point mass filter based on Bayesian estimation is employed as a TRN algorithm. Homograpies are established to estimate the vehicle’s relative translational motion using ground features with simple assumptions. And the error analysis in homography estimation is explored to estimate the error covariance matrix associated with the visual odometry data. The estimated error covariance is delivered to the TRN algorithm for robust estimation. Furthermore, multiple ground features tracked by image observations are utilized as multiple height measurements to improve the performance of the VATRN algorithm.

A SQUID Magnetometer-Based Readout System for an Inertial Navigation System

2020 ◽  
Vol 75 (4) ◽  
pp. 336-341
Author(s):  
A. V. Rzhevskiy ◽  
O. V. Snigirev ◽  
Yu. V. Maslennikov ◽  
V. Yu. Slobodchikov
Keyword(s):  
Navigation System ◽  
Inertial Navigation System ◽  
Inertial Navigation ◽  
Squid Magnetometer ◽  
Readout System
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