Abstract
Unmanned Aerial Vehicles (UAVs) have become a prominent research field due to their vast applicability and reduced size. An appealing aspect of theUAVs is the ability to accomplish autonomous flights in several contexts and purposes, and a variety of applications have been developed, from military to civilian fields. The system proposed in this work is a novel and simplified interaction between the user and the UAV for autonomous flight, where the necessary computation is performed in an embedded computer, decreasing response time and eliminating the necessity of long-distance communication links with base stations. Results are presented with both hardware in the loop simulations and a real UAV using Pixhawk, and Odroid and ROS as companion computer and software platform for code development. © Springer International Publishing Switzerland 2017.

Abstract
Bin picking is a challenging task involving many research domains within the perception and grasping fields, for which there are no perfect and reliable solutions available that are applicable to a wide range of unstructured and cluttered environments present in industrial factories and logistics centers. This paper contributes with research on the topic of object segmentation in cluttered scenarios, independent of previous object shape knowledge, for textured and textureless objects. In addition, it addresses the demand for extended datasets in deep learning tasks with realistic data. We propose a solution using a Mask R-CNN for 2D object segmentation, trained with real data acquired from a RGB-D sensor and synthetic data generated in Blender, combined with 3D point-cloud segmentation to extract a segmented point cloud belonging to a single object from the bin. Next, it is employed a re-configurable pipeline for 6-DoF object pose estimation, followed by a grasp planner to select a feasible grasp pose. The experimental results show that the object segmentation approach is efficient and accurate in cluttered scenarios with several occlusions. The neural network model was trained with both real and simulated data, enhancing the success rate from the previous classical segmentation, displaying an overall grasping success rate of 87.5%.