State estimation for aerial vehicles in forest environments
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Universidade Federal de Minas Gerais
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Autonomous 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.
Abstract
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Robôs - Sistemas de controle, Veículos autônomos
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Measurement by laser beam , Global navigation satellite system , Forestry , Sensors , Laser beams , Feature extraction , Measurement uncertainty, Aerial 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
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https://ieeexplore.ieee.org/abstract/document/8797822