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.

High-Throughput Biomass Estimation in Rice Crops Using UAV Multispectral Imagery The International Center for Tropical Agriculture (CIAT) believes that open access contributes to its mission of reducing hunger and poverty, and improving human nutrition in the tropics through research aimed at increasing the eco-efficiency of agriculture. CIAT is committed to creating and sharing knowledge and information openly and globally. We do this through collaborative research as well as through the open sharing of our data, tools, and publications.

Abstract-This article presents a real time Unmanned Aerial Vehicles UAVs 3D pose estimation method using planar object tracking, in order to be used on the control system of a UAV. The method explodes the rich information obtained by a projective transformation of planar objects on a calibrated camera. The algorithm obtains the metric and projective components of a reference object (landmark or helipad) with respect to the UAV camera coordinate system, using a robust real time object tracking based on homographies. The algorithm is validated on real flights that compare the estimated data against that obtained by the inertial measurement unit IMU, showing that the proposed method robustly estimates the helicopter's 3D position with respect to a reference landmark, with a high quality on the position and orientation estimation when the aircraft is flying at low altitudes, a situation in which the GPS information is often inaccurate. The obtained results indicate that the proposed algorithm is suitable for complex control tasks, such as autonomous landing, accurate low altitude positioning and dropping of payloads.

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This paper presents an implementation of an aircraft pose and motion estimator using visual systems as the principal sensor for controlling an Unmanned Aerial Vehicle (UAV) or as a redundant system for an Inertial Measure Unit (IMU) and gyros sensors. First, we explore the applications of the unified theory for central catadioptric cameras for attitude and heading estimation, explaining how the skyline is projected on the catadioptric image and how it is segmented and used to calculate the UAV's attitude. Then we use appearance images to obtain a visual compass, and we calculate the relative rotation and heading of the aerial vehicle. Additionally, we show the use of a stereo system to calculate the aircraft height and to measure the UAV's motion. Finally, we present a visual tracking system based on Fuzzy controllers working in both a UAV and a camera pan and tilt platform. Every part is tested using the UAV COL-IBRI platform to validate the different approaches, which include comparison of the estimated data with the inertial values measured onboard the helicopter platform and the validation of the tracking schemes on real flights.

Computer visión is much more than a technique to sense and recover environmental information from an UAV. It should play a main role regarding UAVs' functionality because oí the big amount oí information that can be extracted, its possible uses and applications, and its natural connection to human driven tasks, taking into account that visión is our main interface to world understanding. Our current research's focus lays on the development of techniques that allow UAVs to maneuver in spaces using visual information as their main input source. This task involves the creation of techniques that allow an UAV to maneuver towards features of interest whenever a GPS signal is not reliable or sufficient, e.g. when signal dropouts occur (which usually happens in urban áreas, when flying through terrestrial urban canyons or when operating on remote planetary bodies), or when tracking or inspecting visual targets-including moving ones-without knowing their exact UMT coordinates. This paper also investigates visual servoing control techniques that use velocity and position of suitable image features to compute the references for flight control. This paper aims to give a global view of the main aspects related to the research field of computer visión for UAVs, clustered in four main active research lines: visual servoing and control, stereo-based visual navigation, image processing algorithms for detection and tracking, and visual SLAM. Finally, the results of applying these techniques in several applications are presented and discussed: this

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