Abstract
Unmanned aerial vehicle (UAV) is a technology employed for several applications nowadays. One important UAV research topic is the autonomous navigation (AN). The standard procedure for AN is to fuse the signals from an inertial navigation system (INS) and from a global navigation satellite system (GNSS). Our approach to perform the autonomous navigation uses a computer vision system, instead of GNSS signal, associated to the visual odometry. The two techniques applied to estimate the UAV position are combined by a non-extensive particle filter. However, the development of a computer vision system for estimating the UAV position in a situation of flight over water-covered areas and flight in low light conditions is a challenge. Our approach uses images from an active sensor called light detection and ranging (LiDAR) to allow the flight in such conditions.
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Authors want to thank to CNPq, Capes, and Fapesp, Brazilian agencies for research support.
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Braga, J.R.G., de Campos Velho, H.F., Shiguemori, E.H. (2020). Lidar and Non-extensive Particle Filter for UAV Autonomous Navigation. In: Llanes Santiago, O., Cruz Corona, C., Silva Neto, A., Verdegay, J. (eds) Computational Intelligence in Emerging Technologies for Engineering Applications. Studies in Computational Intelligence, vol 872. Springer, Cham. https://doi.org/10.1007/978-3-030-34409-2_13
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