Last edited by Shaktilmaran
Friday, July 31, 2020 | History

1 edition of Semantic 3D Object Maps for Everyday Robot Manipulation found in the catalog.

Semantic 3D Object Maps for Everyday Robot Manipulation

by Radu Bogdan Rusu

  • 7 Want to read
  • 14 Currently reading

Published by Springer Berlin Heidelberg, Imprint: Springer in Berlin, Heidelberg .
Written in English

    Subjects:
  • Image Processing and Computer Vision,
  • Engineering,
  • Robotics and Automation,
  • Image and Speech Processing Signal,
  • Computer vision,
  • Artificial intelligence,
  • Artificial Intelligence (incl. Robotics)

  • About the Edition

    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.

    Edition Notes

    Statementby Radu Bogdan Rusu
    SeriesSpringer Tracts in Advanced Robotics -- 85
    ContributionsSpringerLink (Online service)
    Classifications
    LC ClassificationsTJ210.2-211.495, T59.5
    The Physical Object
    Format[electronic resource] /
    PaginationXII, 232 p. 181 illus.
    Number of Pages232
    ID Numbers
    Open LibraryOL27087011M
    ISBN 109783642354793

      We explore ways the robot can perform semantic mapping of such information while simultaneously identifying and locating objects [7] (see Figure. 8). During a later time of the mapping process, the acquired knowledge is used to avoid ambiguity in perceiving or searching the objects. Semantic Maps Problem definition •Historically informal Self-Organizing Semantic Maps [Ritter and Kohonen, ] Towards semantic maps for mobile robots [Nüchter, Hertzberg , ] Multi-Hierarchical Semantic Maps for Mobile Robotics [Galindo, Buschka , ] and PhD thesis [Pronobis, ] did not provide a formal definition.

    The semantic object maps presented in this article, which we call SOM +, extend the first generation of SOMs presented by Rusu et al. [1] in that the representation of SOM + is designed more thoroughly and that SOM + also include knowledge about the appearance and articulation of furniture objects.   Following our previous work, we represent the environment of the robot with semantic maps. These maps describe not only the geometric properties of the environment, but also describe semantic categories like cooking top using an ontology. Similarly, the everyday objects that are to be manipulated by the robot are described within the ontology.

    spatial and ontological reasoning about objects in the robot’s surroundings. This article contributes a framework for semantic map representation, called SEMAP, to overcome this missing aspect. It is able to manage full 3D maps with geometric object models and the corresponding semantic annotations as well as their relative spatial relations. These solutions include new algorithms for dynamic 3D scene understanding that can build geometric and semantic 3D maps of the environment, even when objects move independently from the camera. Another key breakthrough is the creation of a speech interface that promises to make it easier for humans to collaborate with robots.


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Semantic 3D Object Maps for Everyday Robot Manipulation by Radu Bogdan Rusu Download PDF EPUB FB2

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.

Get this from a library. Semantic 3D object maps for everyday robot manipulation. [Radu Bogdan Rusu] -- 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.

DOI: /s Corpus ID: Semantic 3D Object Maps for Everyday Manipulation in Human Living Environments @article{RusuSemantic3O, title={Semantic 3D Object Maps for Everyday Manipulation in Human Living Environments}, author={Radu Bogdan Rusu}, journal={KI - K{\"u}nstliche Intelligenz}, year={}, volume={24}, pages={} }.

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 Cited by: Request PDF | Semantic 3D Object Maps for Everyday Manipulation in Human Living Environments | Environment models serve as important resources for an autonomous robot by providing it with the Author: Radu Bogdan Rusu.

"Semantic 3D Object Maps for Everyday Manipulation in Human Living Environments" is a Ph. thesis by Radu B. Rusu, in visual perception for robots. This thesis proposes Semantic 3D Object Models as a novel representation of the robot's operating environment that suffice the needs of autonomous robotic platforms, and shows how the relevant.

and acquisition of Semantic Objects Maps (SOMs) that can serve as information resources for autonomous service robots performing everyday manipulation tasks in kitchen environ-ments.

These maps provide the robot with information about its operation environment that enable it to perform fetch and place tasks more efficiently and reliably. Fig. Semantic 3D Object Map of an indoor kitchen environment.

The representative planar areas are shown in different colors (tables - orange, floor - dark purple, walls - green and red, ceiling - cyan), and 3D cuboid containers are marked with their appropriated labels (cupboard, drawer, oven, etc).

DOI: /IROS Corpus ID: Autonomous semantic mapping for robots performing everyday manipulation tasks in kitchen environments @article{BlodowAutonomousSM, title={Autonomous semantic mapping for robots performing everyday manipulation tasks in kitchen environments}, author={Nico Blodow and Lucian Cosmin Goron and Zoltan-Csaba Marton and Dejan.

Environment models serve as important resources for an autonomous robot by providing it with the necessary task-relevant information about its habitat. Their use enables robots to perform their tasks more reliably, flexibly, and efficiently. As autonomous robotic platforms get more sophisticated manipulation capabilities, they also need more expressive and comprehensive environment models:.

[PDF] Approaches to Probabilistic Model Learning for Mobile Manipulation Robots (Springer Tracts. CiteSeerX - Document Details (Isaac Councill, Lee Giles, Pradeep Teregowda): Abstract — In this article we investigate the representation and acquisition of Semantic Objects Maps (SOMs) that can serve as information resources for autonomous service robots performing everyday manipulation tasks in kitchen environments.

These maps provide the robot with information about its operation. independent from the non-object parts of the map. This enables more advanced scene understanding, e.g.

a robot can reason that all 3D points belonging to one object in the map will move together upon manipulation. This object-centric approach is supported by an instance-level semantic segmentation technique that combines bound. We design an example based planning framework to generate semantic grasps, stable grasps that are functionally suitable for specific object manipulation tasks.

We propose to use partial object geometry, tactile contacts, and hand kinematic data as proxies to encode semantic constraints, which are task-related constraints. We introduce a semantic affordance map, which relates local geometry [ ]. The structure of an everyday object describes component parts of the object in terms of simple shape primitives to provide geometrical information and describes connections between parts with kinematic attributes.

The information on the structure is used to map the manipulation knowledge onto the 3D point cloud data. The manipulation knowledge. 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.

We present an example-based planning framework to generate semantic grasps, stable grasps that are functionally suitable for specific object manipulation propose to use partial object geometry, tactile contacts, and hand kinematic data as proxies to encode task-related constraints, which we call semantic introduce a semantic affordance map, which relates local geometry.

Semantic Map Augmentation for Robot Navigation 3 Fig Augmented semantic mapping overview. (a) Bird’s-eye view of the 2D map and door locations (in green) and some image frames of the dataset; (b) Object detection examples; and (c) Visualizations of the augmented semantic map output.

Recently, cheap 3D sensor such as Kinect is widely used in diverse applications. In this paper, we use Kinect as the 3D sensor for the object manipulation using robot arm.

Our target is insert objects on the horizontal table into the corresponding location on the hole in the vertical plate. First, we find two dominant planes corresponding to the horizontal and vertical table by processing 3D.

Semantic 3d object maps for everyday manipulation in human living environments. RB Rusu. KI-Künstliche Intelligenz 24 (4), IEEE/RSJ International Conference on Intelligent Robots and SystemsTowards 3D point cloud based object maps for household environments. RB Rusu, ZC Marton, N Blodow, M Dolha, M Beetz.

manipulation with known objects — for example, folding towels [17], baking cookies [18], or planar contact manipula- presented the robot with the object placed within reach from [33]R. B. Rusu, “Semantic 3d object maps for everyday manipulation in human living environments,” KI-Kunstliche Intelligenz¨,   Semantic maps augment metric-topological maps with meta-information, i.e.

l semantic knowledge aimed at the planning and execution of high-level robotic tasks. Semantic knowledge typically encodes human-like concepts, like types of objects and rooms, which are connected to sensory data when symbolic representations of percepts from the robot workspace are grounded to those concepts.

In this video we demonstrate our efforts to equip service robots with the capability to acquire 3D semantic maps. The robot autonomously explores indoor environments through the .