Proceedings ArticleDOI
Fast 3D recognition and pose using the Viewpoint Feature Histogram
Radu Bogdan Rusu,Gary Bradski,Romain Thibaux,John Hsu +3 more
- pp 2155-2162
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TLDR
The Viewpoint Feature Histogram (VFH) is presented, a descriptor for 3D point cloud data that encodes geometry and viewpoint that is robust to large surface noise and missing depth information in order to work reliably on stereo data.Abstract:
We present the Viewpoint Feature Histogram (VFH), a descriptor for 3D point cloud data that encodes geometry and viewpoint. We demonstrate experimentally on a set of 60 objects captured with stereo cameras that VFH can be used as a distinctive signature, allowing simultaneous recognition of the object and its pose. The pose is accurate enough for robot manipulation, and the computational cost is low enough for real time operation. VFH was designed to be robust to large surface noise and missing depth information in order to work reliably on stereo data.read more
Citations
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Journal ArticleDOI
Deep learning for detecting robotic grasps
TL;DR: This work presents a two-step cascaded system with two deep networks, where the top detections from the first are re-evaluated by the second, and shows that this method improves performance on an RGBD robotic grasping dataset, and can be used to successfully execute grasps on two different robotic platforms.
Proceedings ArticleDOI
LeGO-LOAM: Lightweight and Ground-Optimized Lidar Odometry and Mapping on Variable Terrain
Tixiao Shan,Brendan Englot +1 more
TL;DR: A lightweight and ground-optimized lidar odometry and mapping method, LeGO-LOAM, for realtime six degree-of-freedom pose estimation with ground vehicles and integrated into a SLAM framework to eliminate the pose estimation error caused by drift is integrated.
Journal ArticleDOI
Data-Driven Grasp Synthesis—A Survey
TL;DR: A review of the work on data-driven grasp synthesis and the methodologies for sampling and ranking candidate grasps and an overview of the different methodologies are provided, which draw a parallel to the classical approaches that rely on analytic formulations.
Proceedings Article
Deep Learning for Detecting Robotic Grasps
TL;DR: In this paper, a two-step cascaded system with two deep networks is proposed to detect robotic grasps in an RGB-D view of a scene containing objects, where the top detections from the first are re-evaluated by the second.
Journal ArticleDOI
Learning human activities and object affordances from RGB-D videos
TL;DR: In this paper, a structural support vector machine (SSVM) was used to extract a descriptive labeling of the sequence of sub-activities being performed by a human, and more importantly, their interactions with the objects in the form of associated affordances.
References
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Proceedings ArticleDOI
Fast Point Feature Histograms (FPFH) for 3D registration
TL;DR: This paper modifications their mathematical expressions and performs a rigorous analysis on their robustness and complexity for the problem of 3D registration for overlapping point cloud views, and proposes an algorithm for the online computation of FPFH features for realtime applications.
Proceedings Article
Fast approximate nearest neighbors with automatic algorithm configuration
Marius Muja,David G. Lowe +1 more
TL;DR: A system that answers the question, “What is the fastest approximate nearest-neighbor algorithm for my data?” and a new algorithm that applies priority search on hierarchical k-means trees, which is found to provide the best known performance on many datasets.
Proceedings ArticleDOI
Design and use paradigms for Gazebo, an open-source multi-robot simulator
Nathan Koenig,Andrew Howard +1 more
TL;DR: Gazebo is designed to fill this niche by creating a 3D dynamic multi-robot environment capable of recreating the complex worlds that would be encountered by the next generation of mobile robots.
Journal ArticleDOI
Using spin images for efficient object recognition in cluttered 3D scenes
Andrew E. Johnson,Martial Hebert +1 more
TL;DR: In this paper, a 3D shape-based object recognition system for simultaneous recognition of multiple objects in scenes containing clutter and occlusion is presented, which is based on matching surfaces by matching points using the spin image representation.
Journal ArticleDOI
Shape distributions
TL;DR: The dissimilarities between sampled distributions of simple shape functions provide a robust method for discriminating between classes of objects in a moderately sized database, despite the presence of arbitrary translations, rotations, scales, mirrors, tessellations, simplifications, and model degeneracies.