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Recent work have successfully employed a low-cost Multisensor Data Fusion application based on Hybrid Adaptive Computational Intelligence (HACI) — the cascaded use of Fuzzy-based Computational Intelligence algorithms. The methodology has been shown able to improve considerably the accuracy of current positioning estimation systems for real-time Unmanned Aerial Vehicle (UAV) autonomous navigation — which are not robust — reducing the error in more than 45.19%. However, HACI methodology was found to have a sensitivity to noise in some parts of the estimated trajectories and, therefore, loss of performance. The problem is that none of these recent work assesses the impact of noise present on input signals and the potential benefits of their treatment prior to fusion itself. This is the main contribution of this work. Noise treatment is performed in two approaches: noise removal and noise filtering. It has been shown that for the studied dataset, the noise has a negative impact and that the chosen techniques are capable of adequately handling the noise so as to improve the original GPS precision by almost 57%. [ABSTRACT FROM AUTHOR]

During the last decades, with the increasing use of unmanned aerial vehicles (UAVs), applications that require automatic processing have been growing. One relevant process is the automatic estimation of digital elevation models (DEMs) for specific areas, for example, to support autonomous navigation. In many cases, autonomous flights are desired. Successful autonomous aerial navigation is dependent on several factors, such as UAV position information. In this case, some methods can be employed, combining Global Navigation Satellite System (GNSS) with Inertial Navigation Systems (INSs). When no GNSS and INS signals are available, computer vision has been an alternative for the UAV position estimation. This letter presents a novel approach to automatically estimate DEM from aerial images. Its major contribution is the use of characteristic points, the Zernike Moment Algorithm (ZMA), and the particle collision algorithm to find the correspondences between aerial images. [ABSTRACT FROM AUTHOR]