Visual 3-D SLAM from UAVs

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

Visual Servoing on Image Maps

In this paper we consider the problem of servoing wheeled vehicles in an indoor, initially unknown environment. The proposed approach relies on a hybrid (metric and topological) map built on visual cues. Navigation is planned using topological information to trace a path through viapoints that can be robustly performed by visual servoing control to accurately reach the goal positions. A map of an unknown environment is built as acollection of images taken by an exploratory robot. Images represent nodes in a navigation graph, in which edges represent feasible paths that the robot can execute by visual servoing. Metric and topological information are stored in a hybrid map, which can be shared and cooperatively updated in real time by groups of robots. The merit of the proposed approach is to combine the accuracy of visual servoing methods with a reliable representation of an unknown environment. As a result, the method provides purely visual-based solutions to two of the most relevant problems involved respectively in the field of localization, that is the kidnapped robot problem, and in the field of mapping, that is the closed path detection problem. Experimental results on a laboratory setup are reported, showing the practicality of the proposed approach