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Visual Perception for Humanoid Robots - Environmental Recognition and Localization, from Sensor Signals to Reliable 6D Poses (Hardcover, 1st ed. 2019)
Loot Price: R3,041
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Visual Perception for Humanoid Robots - Environmental Recognition and Localization, from Sensor Signals to Reliable 6D Poses (Hardcover, 1st ed. 2019)
Series: Cognitive Systems Monographs, 38
Expected to ship within 10 - 15 working days
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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 experiments are presented in self-contained chapters
with comparisons and the state of the art. The algorithms were
implemented and empirically evaluated on two humanoid robots: ARMAR
III-A & B. The excellent robustness, performance and derived
results received an award at the IEEE conference on humanoid robots
and the contributions have been utilized for numerous visual
manipulation tasks with demonstration at distinguished venues such
as ICRA, CeBIT, IAS, and Automatica.
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