The aim of the paper is to present, test and discuss the implementation of Visual SLAM techniques to images taken from Unmanned Aerial Vehicles (UAVs) outdoors, in partially structured environments. Every issue of the whole process is discussed in order to obtain more accurate localization and mapping from UAVs flights. Firstly, the issues related to the visual features of objects in the scene, their distance to the UAV, and the related image acquisition system and their calibration are evaluated for improving the whole process. Other important, considered issues are related to the image processing techniques, such as interest point detection, the matching procedure and the scaling factor. The whole system has been tested using the COLIBRI mini UAV in partially structured environments. The results that have been obtained for localization, tested against the GPS information of the flights, show that Visual SLAM delivers reliable localization and mapping that makes it suitable for some outdoors applications when flying UAVs.

This work presents a robust visual localization technique based on an omnidirectional monocular sensor for mobile robotics applications. We intend to overcome the non-linearities and instabilities that the camera projection systems typically introduce, which are especially relevant in catadioptric sensors. In this paper, we come up with several contributions. First, a novel strategy for the uncertainty management is developed, which accounts for a realistic visual localization technique, since it dynamically encodes the instantaneous variations and drifts on the uncertainty, by defining an information metric of the system. Secondly, an epipolar constraint adaption to the omnidirectional geometry reference is devised. Thirdly, Bayesian considerations are also implemented, in order to produce a final global metric for a consistent feature matching between images. The resulting outcomes are supported by real data experiments performed with publicly-available datasets, in order to assess the suitability of the approach and to confirm the reliability of the main contributions. Besides localization results, real visual SLAM (Simultaneous Localization and Mapping) comparison experiments with acknowledged methods are also presented, by using a public dataset and benchmark framework.

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