{"product_id":"9783319978390","title":"Cognitive Systems Monographs: Environmental Recognition and Localization, from Sensor Signals to Reliable 6D Poses","description":"\u003ch1\u003eCognitive Systems Monographs: Environmental Recognition and Localization, from Sensor Signals to Reliable 6D Poses\u003c\/h1\u003e \u003ch2\u003eGonzález Aguirre, David Israel\u003c\/h2\u003e \u003cp\u003eThis book provides an overview of model-based environmental visual perception for humanoid robots. The visual perception of a humanoid robot creates a \u003ci\u003ebidirectional bridge\u003c\/i\u003e connecting sensor signals with internal representations of environmental objects. The objective of such perception systems is to answer two fundamental questions: \u003ci\u003eWhat \u0026amp; where is it?\u003c\/i\u003e To answer these questions using a \u003ci\u003esensor-to-representation bridge\u003c\/i\u003e, coordinated processes are conducted to extract and exploit cues matching robot’s mental representations to physical entities. These include sensor \u0026amp; actuator modeling, calibration, filtering, and feature extraction for state estimation. This book discusses the following topics in depth:\u003cb\u003e \u003c\/b\u003e\u003c\/p\u003e\u003cp\u003e•     \u003cb\u003eActive Sensing\u003c\/b\u003e: 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. \u003cbr\u003e\u003c\/p\u003e\u003cp\u003e•     \u003cb\u003eFeature Extraction \u0026amp; Recognition\u003c\/b\u003e: 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 \u0026amp; low-resolution images. Stereoscopic vision, Euclidean metric and graph-shape descriptors are shown to be powerful mechanisms for difficult recognition tasks. \u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e•     \u003cb\u003eGlobal Self-Localization \u0026amp; Depth Uncertainty Learning\u003c\/b\u003e: 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.\u003c\/p\u003e\u003cp\u003eThe 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: \u003ci\u003eARMAR III-A \u0026amp; B\u003c\/i\u003e. 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 \u003ci\u003eICRA, CeBIT, IAS, and Automatica.\u003c\/i\u003e\u003c\/p\u003e \u003ch3\u003eDetails\u003c\/h3\u003e \u003cp\u003ePublished by: Springer\u003c\/p\u003e \u003cp\u003ePublication Date: 2018-09-11\u003c\/p\u003e \u003cp\u003eFormat: Hardcover\u003c\/p\u003e \u003cp\u003eISBN-13: 9783319978390\u003c\/p\u003e \u003cp\u003eDOI: 10.1007\/978-3-319-97841-3\u003c\/p\u003e \u003cp\u003eDimensions: 235cm x155cm\u003c\/p\u003e \u003cp\u003ePages: 220\u003c\/p\u003e ","brand":"Springer International Publishing","offers":[{"title":"Default Title","offer_id":45382788415628,"sku":"9783319978390","price":98.99,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0710\/9545\/1788\/files\/9783319978390.jpg?v=1772894843","url":"https:\/\/fh90cf-fv.myshopify.com\/products\/9783319978390","provider":"Late Knight Books and Services, LLC","version":"1.0","type":"link"}