State estimation for aerial vehicles in forest environments

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dc.creatorAntônio Carlos Bana Chiella
dc.creatorBruno Otávio S. Teixeira
dc.creatorGuilherme Augusto Silva Pereira
dc.date.accessioned2025-05-06T14:38:33Z
dc.date.accessioned2025-09-08T23:26:42Z
dc.date.available2025-05-06T14:38:33Z
dc.date.issued2019
dc.identifier.doi10.1109/ICUAS.2019.8797822
dc.identifier.urihttps://hdl.handle.net/1843/82054
dc.languageeng
dc.publisherUniversidade Federal de Minas Gerais
dc.relation.ispartofInternational Conference on Unmanned Aircraft Systems (ICUAS)
dc.rightsAcesso Restrito
dc.subjectRobôs - Sistemas de controle
dc.subjectVeículos autônomos
dc.subject.otherMeasurement by laser beam , Global navigation satellite system , Forestry , Sensors , Laser beams , Feature extraction , Measurement uncertainty
dc.subject.otherAerial Vehicles , Global Navigation Satellite System , Tree Trunks , Fusion Algorithm , Unscented Kalman Filter , Covariance Matrix , Relative Measure , Related Information , Measurement Uncertainty , Position Information , Global Measures , Absolute Measures , Position Estimation , Coordinate Frame , Influence Of Outliers , Adaptive Filter , Motion Estimation , Vehicle State , Slow Drift , Measurement Outliers , Micro Air Vehicles , Robot Operating System , Rotation Vector , Robust Filter , Velocity Estimation , Process Noise , Time-based , Set Of Equations , First-order Differential Equations
dc.titleState estimation for aerial vehicles in forest environments
dc.typeArtigo de evento
local.citation.epage890
local.citation.spage882
local.description.resumoAutonomous navigation of unnamed vehicles in a forest is a challenging task. In such environments, due to the canopies of the trees, GNSS-based navigation can be degraded or even unavailable. In this paper we propose a state estimation solution for aerial vehicles based on the fusion of GNSS, AHRS and LIDAR-based odometry. In our LIDAR odometry solution, the trunks of the trees are used in a feature-based scan-matching algorithm to estimate the relative movement of the vehicle. Our method uses a robust adaptive fusion algorithm based on the unscented Kalman filter. Experimental data collected during the navigation of a quadrotor in an actual forest environment is used to demonstrate the effectiveness of our approach.
local.publisher.countryBrasil
local.publisher.departmentENG - DEPARTAMENTO DE ENGENHARIA ELÉTRICA
local.publisher.departmentENG - DEPARTAMENTO DE ENGENHARIA ELETRÔNICA
local.publisher.initialsUFMG
local.url.externahttps://ieeexplore.ieee.org/abstract/document/8797822

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