LEADER 05144nam 22006135 450 001 9910484145603321 005 20200702062114.0 010 $a3-319-97841-1 024 7 $a10.1007/978-3-319-97841-3 035 $a(CKB)4100000006098343 035 $a(MiAaPQ)EBC5508016 035 $a(DE-He213)978-3-319-97841-3 035 $z(PPN)258858427 035 $a(PPN)243769865 035 $a(EXLCZ)994100000006098343 100 $a20181108d2019 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aVisual Perception for Humanoid Robots $eEnvironmental Recognition and Localization, from Sensor Signals to Reliable 6D Poses /$fby David Israel González Aguirre 205 $a1st ed. 2019. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2019. 215 $a1 online resource (253 pages) 225 1 $aCognitive Systems Monographs,$x1867-4925 ;$v38 311 $a3-319-97839-X 327 $aIntroduction -- 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. 330 $aThis 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. 410 0$aCognitive Systems Monographs,$x1867-4925 ;$v38 606 $aComputational intelligence 606 $aRobotics 606 $aAutomation 606 $aArtificial intelligence 606 $aOptical data processing 606 $aComputational Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/T11014 606 $aRobotics and Automation$3https://scigraph.springernature.com/ontologies/product-market-codes/T19020 606 $aArtificial Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/I21000 606 $aImage Processing and Computer Vision$3https://scigraph.springernature.com/ontologies/product-market-codes/I22021 615 0$aComputational intelligence. 615 0$aRobotics. 615 0$aAutomation. 615 0$aArtificial intelligence. 615 0$aOptical data processing. 615 14$aComputational Intelligence. 615 24$aRobotics and Automation. 615 24$aArtificial Intelligence. 615 24$aImage Processing and Computer Vision. 676 $a629.892637 700 $aGonzález Aguirre$b David Israel$4aut$4http://id.loc.gov/vocabulary/relators/aut$01226804 906 $aBOOK 912 $a9910484145603321 996 $aVisual Perception for Humanoid Robots$92848580 997 $aUNINA