LEADER 00871nam0-2200277 --450 001 9910415660303321 005 20200914104031.0 010 $a978-88-85622-22-7 100 $a20200914d2018----km y0itay50 ba 101 0 $aita 102 $aIT 105 $a a 001yy 200 1 $aLibro bianco$egenerazione Proteo$fa cura di Nicola Ferrigni$gcontributi di: Stefano Arduini ... [et al.] 210 $aRoma$cEurilink University press$d2018 215 $a471 p.$cill.$d24 cm 225 1 $aIstituzioni$v11 300 $aIn testa al front.: Link Campus University; Osservatorio generazione Proteo 702 1$aArduini,$bStefano 702 1$aFerrigni,$bNicola 801 0$aIT$bUNINA$gREICAT$2UNIMARC 901 $aBK 912 $a9910415660303321 952 $aCOLLEZ. 2553 (11)$b1358/2020$fFSPBC 959 $aFSPBC 996 $aLibro bianco$9718989 997 $aUNINA LEADER 05385nam 2200649 a 450 001 9910457458803321 005 20200520144314.0 010 $a1-283-43325-7 010 $a9786613433251 010 $a981-283-636-5 035 $a(CKB)2550000000079545 035 $a(EBL)840678 035 $a(OCoLC)858228279 035 $a(SSID)ssj0000645204 035 $a(PQKBManifestationID)12206475 035 $a(PQKBTitleCode)TC0000645204 035 $a(PQKBWorkID)10680711 035 $a(PQKB)10003121 035 $a(MiAaPQ)EBC840678 035 $a(WSP)00007080 035 $a(Au-PeEL)EBL840678 035 $a(CaPaEBR)ebr10524533 035 $a(CaONFJC)MIL343325 035 $a(EXLCZ)992550000000079545 100 $a20111110d2011 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aCluster computing for robotics and computer vision$b[electronic resource] /$fDamian M. Lyons 210 $aSingapore $cWorld Scientific$dc2011 215 $a1 online resource (235 p.) 300 $aDescription based upon print version of record. 311 $a981-283-635-7 320 $aIncludes bibliographical references (p. 199-206) and index. 327 $aDedication; Preface; Contents; List of Tables; List of Figures; 1. Introduction; 1.1 Robots; 1.2 Cluster Computing; 1.3 Overview of the Book; 2. Clusters and Robots; 2.1 Parallel Computation; 2.1.1 Parallel Architectures; 2.1.2 Multiprocessor; 2.1.3 Multicomputer; 2.2 Clusters; 2.2.1 Terminology; 2.2.2 Cluster Configuration; 2.2.3 Programming the Cluster; 2.2.4 Configuring the Cluster; 2.2.5 Simple Cluster Configuration with OpenMPI; 2.2.6 Connecting the Cluster to the Robot; 2.3 Summary; References; 3. Cluster Programming; 3.1 Approaches to Parallel Programming; 3.2 Programming with MPI 327 $a3.2.1 Message-Passing3.2.2 Single Program Multiple Data (SPMD) Model; 3.2.3 Collective Communication; 3.3 Compiling and Running MPI Programs; 3.4 Analyzing Parallel Computation Time; 3.4.1 Speedup and Amdhal'sLaw; 3.4.2 Communication and Calculation; 3.4.3 Communication Models; 3.5 Summary; References; 4. Robot Motion; 4.1 Motion of a Mobile Robot in Two Dimensions; 4.2 Calculation of Location by Dead-Reckoning; 4.2.1 Partitioning: Block Data Decomposition; 4.2.2 Program Design; 4.2.3 Analysis; 4.3 Dead-Reckoning with Intermediate Results; 4.3.1 Partitioning; 4.3.2 Program Design 327 $a4.3.3 Analysis4.4 Dead-Reckoning for a Team of Robots; 4.4.1 Partitioning; 4.4.2 Program Design; 4.4.3 Analysis; 4.4.4 Local and Global Buffers; 4.5 Summary; References; 5. Sensors; 5.1 Transforming Sensor Readings; 5.1.1 Partitioning: Single Robot Location; 5.1.2 Analysis; 5.1.3 Partitioning: Multiple Robot Locations; 5.1.4 Analysis; 5.2 Drawing a Map from Sonar Data; 5.2.1 Finding Straight Lines with the Hough Transform; 5.2.2 Partitioning; 5.2.3 Program Design; 5.2.4 Analysis; 5.2.5 Load Balanced Hough Calculation; 5.2.6 Analysis; 5.3 Aligning Laser Scan Measurements 327 $a5.3.1 Polar Scan Matching5.3.2 Partitioning and Analysis; 5.3.3 Program Design; 5.4 Summary; References; 6. Mapping and Localization; 6.1 Constructing a Spatial Occupancy Map; 6.1.1 Probabilistic Sonar Model; 6.1.2 Bayesian Filtering; 6.1.3 Partitioning by Map; 6.1.4 Program Design; 6.1.4.1. Phase 1; 6.1.4.2. Phase 2; 6.1.4.3. Phase 3; 6.1.4.4. Phase 4; 6.1.5 Analysis; 6.1.6 Partitioning by Sensor Readings; 6.1.7 Program Design; 6.1.8 Analysis; 6.2 Monte-Carlo Localization; 6.2.1 Partitioning; 6.2.2 Program Design; 6.2.3 Analysis; 6.2.4 Improving the Serial Fraction; 6.3 Summary; References 327 $a7. Vision and Tracking7.1 Following the Road; 7.2 Iconic Image Processing; 7.2.1 Partitioning; 7.2.2 Program Design; 7.2.3 Analysis; 7.2.4 Spatial Pixel Operations; 7.2.5 Partitioning; 7.2.6 Program Design; 7.3 Multiscale Image Processing; 7.3.1 Partitioning; 7.4 Video Tracking; 7.4.1 Spatial Histograms; 7.4.2 Condensation; 7.4.3 Partitioning; 7.4.4 Program Design; 7.5 Summary; References; 8. Learning Landmarks; 8.1 Landmark Spatiograms; 8.2 K-Means Clustering; 8.2.1 Partitioning; 8.2.2 Program Design; 8.2.3 Analysis; 8.3 EM Clustering; 8.3.1 Partitioning; 8.3.2 Program Design; 8.3.3 Analysis 327 $a8.4 Summary 330 $aIn this book, we look at how cluster technology can be leveraged to build better robots. Algorithms and approaches in key areas of robotics and computer vision, such as map building, target tracking, action selection and landmark learning, are reviewed and cluster implementations for these are presented. The objective of the book is to give professionals working in the beowulf cluster or robotics and computer vision fields a concrete view of the strong synergy between the areas as well as to spur further fruitful exploitation of this connection. The book is written at a level appropriate for a 606 $aRobotics$xProgramming 606 $aComputer vision$xProgramming 608 $aElectronic books. 615 0$aRobotics$xProgramming. 615 0$aComputer vision$xProgramming. 676 $a629.8925 700 $aLyons$b Damian M$0990453 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910457458803321 996 $aCluster computing for robotics and computer vision$92265846 997 $aUNINA