rdf-syntax-grammar
mods:
  schema location: http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
  title info:
    title: Visual Perception for Humanoid Robots
    sub title: Environmental Recognition and Localization, from Sensor Signals to Reliable 6D Poses
  name:
    name part: González Aguirre, David Israel.
    role:
      role term: creator
    role:
      role term: author.
    role:
      role term: aut
    role:
      role term: http://id.loc.gov/vocabulary/relators/aut
  name:
    name part: SpringerLink (Online service)
  type of resource: text
  origin info:
    place:
      place term: gw
    date issued: 2019
    edition: 1st ed. 2019.
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: electronic
    form: electronic resource
    reformatting quality: access
    form: remote
    extent: XXXVI, 220 p. 126 illus., 109 illus. in color. online resource.
  abstract: This book provides an overview of model-based environmental visual perception for humanoid robots. The visual perception of a humanoid robot creates a bidirectional bridge connecting sensor signals with internal representations of environmental objects. The objective of such perception systems is to answer two fundamental questions: What & where is it? To answer these questions using a sensor-to-representation bridge, coordinated processes are conducted to extract and exploit cues matching robot's mental representations to physical entities. These include sensor & actuator modeling, calibration, filtering, and feature extraction for state estimation. This book discusses the following topics in depth: • Active Sensing: Robust probabilistic methods for optimal, high dynamic range image acquisition are suitable for use with inexpensive cameras. This enables ideal sensing in arbitrary environmental conditions encountered in human-centric spaces. The book quantitatively shows the importance of equipping robots with dependable visual sensing. • Feature Extraction & Recognition: Parameter-free, edge extraction methods based on structural graphs enable the representation of geometric primitives effectively and efficiently. This is done by eccentricity segmentation providing excellent recognition even on noisy & low-resolution images. Stereoscopic vision, Euclidean metric and graph-shape descriptors are shown to be powerful mechanisms for difficult recognition tasks. • Global Self-Localization & Depth Uncertainty Learning: Simultaneous feature matching for global localization and 6D self-pose estimation are addressed by a novel geometric and probabilistic concept using intersection of Gaussian spheres. The path from intuition to the closed-form optimal solution determining the robot location is described, including a supervised learning method for uncertainty depth modeling based on extensive ground-truth training data from a motion capture system. The methods and experiment…
  table of contents: Introduction -- State-of-the-Art -- World Model Representation -- Methods for Robust and Accurate Image Acquisition -- Environmental Visual Object Recognition -- Visual Uncertainty Model of Depth Estimation -- Global Visual Localization -- Conclusion and Future Work -- Bibliography.
  note: by David Israel González Aguirre.
  subject:
    topic: Computational intelligence
  subject:
    topic: Robotics
  subject:
    topic: Automation
  subject:
    topic: Artificial intelligence
  subject:
    topic: Optical data processing
  subject:
    topic: Computational Intelligence
  subject:
    topic: Robotics and Automation
  subject:
    topic: Artificial Intelligence
  subject:
    topic: Image Processing and Computer Vision
  classification: Q342
  classification: 006.3
  related item:
    title info:
      title: Springer eBooks
  related item:
    title info:
      title: Cognitive Systems Monographs, 38
  identifier: 9783319978413
  identifier: https://doi.org/10.1007/978-3-319-97841-3
  record info:
    record creation date: 181108
    record change date: 20191022162526.0
    record identifier: 978-3-319-97841-3