496 research outputs found
A 3D Robot Self Filter for Next Best View Planning
No full textThis paper investigates the use of a real-time self filter for a robot manipulator in next best view planning tasks. The robot is equipped with a depth sensor in eye-in-hand configuration. The goal of the next best view algorithm is to select at each iteration an optimal view pose for the sensor in order to optimize information gain to perform 3D reconstruction of a region of interest. An OpenGL-based filter was adopted, that is able to determine which pixels of the depth image are due to robot self observations. The filter was adapted to work with KinectFusion volumetric based 3D reconstruction. Experiments have been performed in a real scenario. Results indicate that removal of robot self observations prevents artifacts in the final 3D representation of the environment. Moreover, view poses where the robot would occlude the target regions can be successfully avoided. Finally, it is shown that a convex-hull robot model is preferable to a tight 3D CAD model, and that the filter can be integrated with a surfel-based next best view planner with negligible overhead
Surfel-Based Next Best View Planning
No full textNext best view (NBV) planning is a central task for automated three-dimensional (3-D) reconstruction in robotics. The most expensive phase of NBV computation is the view simulation step, where the information gain of a large number of candidate sensor poses are estimated. Usually, information gain is related to the visibility of unknown space from the simulated viewpoint. A well-established technique is to adopt a volumetric representation of the environment and to compute the NBV from ray casting by maximizing the number of unknown visible voxels. This letter explores a novel approach for NBV planning based on surfel representation of the environment. Surfels are oriented surface elements, such as circular disks, without explicit connectivity. A new kind of surfel is introduced to represent the frontier between empty and unknown space. Surfels are extracted during 3-D reconstruction, with minimal overhead, from a KinectFusion volumetric representation. Surfel rendering is used to generate images from each simulated sensor pose. Experiments in a real robot setup are reported. The proposed approach achieves better performance than volumetric algorithms based on ray casting implemented on graphics processing unit (GPU), with comparable results in terms of reconstruction quality. Moreover, surfel-based NBV planning can be applied in larger environments as a volumetric representation is limited by GPU memory
Functional Principal Component Analysis for Recognition of Arm Gestures and Humanoid Imitation
No full text
Visualization of AGV in Virtual Reality and Collision Detection with Large Scale Point Clouds
No full textVirtual reality (VR) will play an important role in the factory of the future. In this paper, an immersive and interactive VR system is presented for3D visualization of automated guided vehicles (AGVs) moving in a warehouse. The environment model consists of a large scale point cloud obtained through a Terrestrial Laser Scanning (TLS) survey. Realistic AGV animation is achieved thanks to the extraction of an accurate model of the ground. Visualization of AGV safety zones is also supported.Moreover, the system enables real-time collision detection between the 3D vehicle model and the point cloud model of the environment. Collision detection is useful for checking the feasibility of a specified vehicle path. Efficient techniques for dynamic loading of massive point cloud data have been developed to speed up rendering and collision detection. The VR system can be used to assist the design of automated warehouses and to show customers what their future industrial plant would look like
Learning Manipulation Tasks from Human Demonstration and 3D Shape Segmentation
No full textAccording to neuro-psychology studies, 3D shape segmentation plays an important role in human perception of objects because when an object is perceived for grasping it is first parsed in its constituent parts. This capability is missing in current robot planning systems, which are therefore hindered in their ability to plan part-specific grasps suitable for the current task. In this paper, a novel approach for part-based grasping is presented that combines 3D shape segmentation, programing by human demonstration and manipulation planning. The central advantage over previous approaches is the use of a topological method for shape segmentation enabling both object categorization and robot grasping according to the affordances of an object. Manipulation tasks are demonstrated in a virtual reality environment using a data glove and a motion tracker, and the specific parts of the objects where grasping occurs are learned and encoded in the task description. Tasks are then planned and executed in a robot environment targeting semantically relevant parts for grasping. Planning in the robot environment can be generalized to objects that are similar to the ones used for task demonstration, i.e. objects that belong to the same category. Results obtained in 3D simulation confirm that the proposed approach finds with less effort grasps appropriate for the requested task
Robot Grasp Synthesis from Virtual Demonstration and Topology-Preserving Environment Reconstruction
No full text- …
