Towards reliable grasping and manipulation in household environments

Abstract We present a complete software architecture for reliable grasping of household objects. Our work combines aspects such as scene interpretation from 3D range data, grasp planning, motion planning, and grasp failure identification and recovery using tactile sensors. We build upon, and add several new contributions to the significant prior work in these areas. A salient feature of our work is the tight coupling between perception (both visual and tactile) and manipulation, aiming to address the uncertainty due to sensor and execution errors. This integration effort has revealed new challenges, some of which can be addressed through system and software engineering, and some of which present opportunities for future research. Our approach is aimed at typical indoor environments, and is validated by long running experiments where the PR2 robotic platform was able to consistently grasp a large variety of known and unknown objects. The set of tools and algorithms for object grasping presented here have been integrated into the open-source Robot Operating System (ROS)

Semantic 3D object maps for everyday robot manipulation

The book written by Dr. Radu B. Rusu presents a detailed description of 3D Semantic Mapping in the context of mobile robot manipulation. As autonomous robotic platforms get more sophisticated manipulation capabilities, they also need more expressive and comprehensive environment models that include the objects present in the world, together with their position, form, and other semantic aspects, as well as interpretations of these objects with respect to the robot tasks.   The book proposes novel 3D feature representations called Point Feature Histograms (PFH), as well as frameworks for the acquisition and processing of Semantic 3D Object Maps with contributions to robust registration, fast segmentation into regions, and reliable object detection, categorization, and reconstruction. These contributions have been fully implemented and empirically evaluated on different robotic systems, and have been the original kernel to the widely successful open-source project the Point Cloud Library (PCL) -- see http://pointclouds.org

.---== = “unpublished papers ” series ===---. ON DATA FUSION METHODS USING NEURAL NETWORKS, FROM A PRACTICAL IMPLEMENTATION POV*

Instead of the usual abstract, I’m using this section to express my gratitude to the open source community. This world truly is a better place to live and study because of them. Inspired by the “open source ” path, I’m starting a series of tutorials, targeted at the people who are just starting out in the field of mobile robotics. The “unpublished papers series ” are just basic tutorials on things that should already be open sourced, but are not (or I couldn’t find them). Their purpose is not to be used as a source of copy&paste, but as a beginning point. Some of them might seem trivial, but remembering how I started to learn, they are not

Point Feature Extraction on 3D Range Scans Taking into Account Object Boundaries

Abstract—In this paper we address the topic of feature extraction in 3D point cloud data for object recognition and pose identification. We present a novel interest keypoint extraction methodthatoperatesonrangeimagesgenerated fromarbitrary 3D point clouds, which explicitly considers the borders of the objects identified by transitions from foreground to background. We furthermore present a feature descriptor that takes the same information into account. We have implemented our approachandpresentrigorousexperimentsinwhichweanalyze the individual components with respect to their repeatability and matching capabilities and evaluate the usefulness for point feature based object detection methods. I

Fast Point Feature Histograms (FPFH) for 3D Registration

Abstract — In our recent work [1], [2], we proposed Point Feature Histograms (PFH) as robust multi-dimensional features which describe the local geometry around a point p for 3D point cloud datasets. In this paper, we modify their mathematical expressions and perform a rigorous analysis on their robustness and complexity for the problem of 3D registration for overlapping point cloud views. More concretely, we present several optimizations that reduce their computation times drastically by either caching previously computed values or by revising their theoretical formulations. The latter results in a new type of local features, called Fast Point Feature Histograms (FPFH), which retain most of the discriminative power of the PFH. Moreover, we propose an algorithm for the online computation of FPFH features for realtime applications. To validate our results we demonstrate their efficiency for 3D registration and propose a new sample consensus based method for bringing two datasets into the convergence basin of a local non-linear optimizer: SAC-IA (SAmple Consensus Initial Alignment). I