Scanning Technologies for Autonomous Systems

• Autore: Rodríguez-Quiñonez Julio C
• Pubblicazione: Cham : , : Springer, , 2024; ©2024
• Edizione: 1st ed.
• Descrizione fisica: 1 online resource (455 pages)
• Altri autori: Flores-FuentesWendy Castro-ToscanoMoises J SergiyenkoOleg
• Nota di contenuto: Intro -- Preface -- Introduction -- An Overview of Scanning Technologies for Autonomous Systems -- Acknowledgments -- Contents -- Editors and Contributors -- About the Editors -- Contributors -- Abbreviations -- Part I Scanning Technologies for Navigation and Area Mapping -- Methodology of Assigning Terrain Object Images to the Class of Landmarks for Autonomous Mobile Robots -- 1 Introduction -- 2 Background -- 3 Detection of Objects as Possible Landmarks -- 4 Landmarks Classification Criteria for Autonomous Mobile Robots -- 4.1 Formation of the Feature Vector for a Landmark Recognition -- 4.2 Methods of Calculating the Hausdorff Distance -- 4.3 Demonstration of Hausdorff Distance (HD) in the Simplest Situations -- 5 Classification of Landmarks Against the Background of Real Terrain Images -- 5.1 Results of Modeling the Hausdorff Distance Between Images of Reference and Real Landmarks Against the Background of Terrain Images -- 5.2 The Method of Determining the Threshold Value of the Hausdorff Distance -- 5.3 Analysis of the Object Classification Quality -- 5.4 Possible Future Research Directions -- 6 Conclusions -- References -- Scanning Systems for Environment Perception in Autonomous Navigation -- 1 Introduction -- 2 Fundamentals of Scanning Systems -- 2.1 Multi-Camera and Stereo Vision Systems -- 2.1.1 Stereo Vision Systems -- 2.2 Laser Scanning Systems -- 2.2.1 Dynamic Laser Triangulation -- 2.2.2 Laser Line Triangulation -- 2.2.3 Light Detection and Ranging -- 2.3 Millimeter Wave Scanning Systems -- 2.4 Ultrasonic Scanning Systems -- 2.5 Overview of Vision Technologies -- 3 Applications of Scanning Systems in Autonomous Navigation -- 3.1 Indoor Navigation -- 3.1.1 Multi-Camera Systems -- 3.1.2 Stereo Vision Systems -- 3.1.3 Laser Line Triangulation -- 3.1.4 Millimeter Wave Scanning Systems -- 3.1.5 Ultrasonic Scanning Systems. ; 3.2 Outdoor Navigation -- 3.2.1 Multi-Camera Systems -- 3.2.2 Stereo Vision Systems -- 3.2.3 Dynamic Laser Triangulation -- 3.2.4 Laser Line Triangulation -- 3.2.5 Light Detection and Ranging -- 3.2.6 Millimeter Wave Scanning Systems -- 3.2.7 Ultrasonic Scanning Systems -- 3.3 Overview of Recent Advancements in Autonomous Navigation Technologies -- 4 Conclusions -- References -- Autonomous Visual 3D Mapping of the Ocean Floor by Underwater Robots Equipped with a Single Photo Camera -- 1 Introduction -- 2 State of the Art and Challenges -- 3 System Design -- 3.1 AUV Platform: Girona-500 -- 3.2 CoraMo Camera and Lighting -- 3.3 Dome Port and Camera Calibration -- 4 Mission Planning and AUV Operations -- 4.1 Mission Parameters -- 4.2 Calibration Maneuvers for Light and Water -- 4.3 Deployment and Mission Monitoring -- 4.4 Raw Data Management and FAIR Data -- 5 Micronavigation and Automated Seafloor Mapping -- 5.1 Color Correction and Correspondences -- 5.2 Sparse 3D Reconstruction -- 5.3 Dense 3D Reconstruction -- 6 Results -- 7 Discussion -- References -- Flexible Multicamera Virtual Focal Plane: A Light-Field Dynamic Homography Approach -- 1 Introduction -- 2 Light-Field Dynamic Homography (LDH) -- 3 Experiments -- 3.1 Simulation Environment -- 4 Conclusions -- References -- Part II Scanning Technologies for Medical and Industrial Applications -- US Scanning Technologies and AI -- 1 Introduction -- 1.1 Ultrasound -- 1.2 Medical Use of Ultrasound -- 1.3 Ultrasound System -- 1.4 Scanning Modes -- 1.5 AI in Medical Ultrasound -- 2 Conclusion -- References -- Optical 3D Scanning System in Medical Applications -- 1 Introduction -- 2 Optical Active Scanners -- 2.1 Laser Scanner -- 2.1.1 Principle of Triangulation -- 2.1.2 Time of Flight -- 2.1.3 Structured Light Techniques -- 2.2 Optical Active Scanner Applied in the Human Body. ; 2.2.1 Laser Scanning Using Time of Flight -- 2.2.2 Laser Scanning Using the Principle of Triangulation -- 2.2.3 Scanner Using Structured Light -- 3 Passive Optical Scanner Based on Cameras -- 3.1 Monovision -- 3.2 Stereovision -- 3.3 Multicamera -- 3.3.1 Linear Method -- 3.3.2 Midpoint Method -- 3.4 Passive Optical Scanner Applied to the Human Body -- 3.4.1 Scanner Using Monovision -- 3.4.2 Scanner Using Stereovision -- 3.4.3 Scanner Using Multicamera -- 4 Conclusion -- References -- Depth Measurement with a Rotating Camera System for Robotic Applications -- 1 Introduction -- 2 Depth from Rotational Stereo -- 3 Incorporating Multiple Rotation Angles for Depth Measurement -- 3.1 Use Multiple Baseline to Eliminate Error -- 3.2 Error Elimination with External Constraints -- 3.3 Disparity Enhancement and Depth Derivation -- 4 Implementation and Experiments -- 4.1 Simulation System -- 4.2 Static Image Acquisition -- 4.3 Semi-Dynamic Image Acquisition -- 4.4 Dynamic Image Acquisition -- 5 Conclusion -- References -- Part III Innovative Data Processing Solutions for Scanning Technologies -- Investigating the Effects of Subsampling Processes to Point Cloud Data on the Generation of 3D Models of Archaeological Monuments -- 1 Introduction -- 2 A Mini Review on the Point Cloud Subsampling -- 3 Methodology -- 3.1 Phase 1-Data Collection Using Terrestrial Laser Scanning Sensor -- 3.2 Phase 2-Point Cloud Data Pre-processing -- 3.3 Phase 3-Point Cloud Cross-Section Process -- 3.4 Phase 4-Point Cloud Subsampling Process -- 3.5 Phase 5-Point Cloud Data Post-Processing -- 3.6 Phase 6-Surface Deviation Process -- 3.6.1 Surface Deviation Results on the Historical Charcoal Chamber Test Object -- 3.6.2 Surface Deviation Results on the Historical Bendang Dalam Temple Test Object -- 3.6.3 Surface Deviation Results on the Old Johor City Museum Test Object. ; 3.7 Phase 7-Analysis Process -- 3.7.1 Analysis Process on Surface Deviation Results on the Historical Charcoal Chamber Test Object -- 3.7.2 Analysis Process on Surface Deviation Results on the Historical Bendang Dalam Temple Test Object -- 3.7.3 Analysis Process on Surface Deviation Results on the Old Johor City Museum Test Object -- 4 Overall Findings -- 5 Conclusion -- References -- Approach to Background Suppression in Scanning Machine Vision Systems -- 1 Introduction -- 2 Background Suppression Technique in the Optical Domain -- 3 False Alarm Reduction Algorithm Based on Cyclic Search -- 4 Optimization of the Cyclic Search Algorithm -- 5 Experimental Results -- 6 Conclusion -- References -- Point Cloud Optimization Employing Multisensory Vision -- 1 Introduction -- 1.1 Machine Vision -- 2 Laser Scanning Systems for Three-Dimensional Information -- 2.1 The Technical Vision System -- 2.2 Dynamic Triangulation Method -- 3 Electromechanical Analysis for Precision Improvement -- 4 Image Processing for Machine Vision -- 4.1 Spatial Information Using Cameras as Sensors -- 4.2 Segmentation for Matching -- 4.3 Deep Learning for Segmentation and Matching -- 5 Multi-View Information Fusion -- 6 Outliers -- 6.1 Identification and Handling of Outlier Data -- 6.1.1 Interquartile Method -- 6.1.2 Modified Tau-Thompson Method -- 6.1.3 Chi2 Distribution Quantiles -- 6.1.4 Cook's Distance -- 6.2 Outlier Classification -- 6.3 Outlier Handling -- 7 Conclusions -- References -- Part IV Sensing Applications Across Systems: Technological Advancements -- Person-Centric Sensing in Indoor Environments -- 1 Introduction -- 2 Optical Modalities -- 2.1 RGB Cameras -- 2.2 Depth Cameras -- 2.3 Thermal Cameras -- 2.4 Advantages and Challenges of a Multi-Modal RGBDT Approach -- 2.5 Visual Privacy -- 3 Blind Modalities -- 3.1 Radar -- 3.2 WiFi -- 3.3 Surface Acoustic. ; 3.4 Environmental Sensors -- 4 Conclusions -- 4.1 Multi-Modal Fusion -- 4.2 Data Representations -- 4.3 Data Processing -- References -- Machine Vision for Solid Waste Detection -- 1 Introduction -- 2 Tasks and Challenges for Machine Vision in the Context of Contemporary Industrial Waste Sorting -- 3 Waste Detection Hardware -- 3.1 From Sensors to Matrices -- 3.1.1 Basic Photoelectric Sensors -- 3.1.2 Complex Photodetectors -- 3.2 Multispectral and Hyperspectral Cameras -- 3.2.1 RGB (Red, Green, Blue) Cameras -- 3.2.2 RGB IR Cameras -- 3.2.3 Multispectral and Hyperspectral Cameras -- 3.3 Video Camera Setup and Synchronization -- 3.4 Waste Detection Systems -- 4 Image Processing -- 4.1 Hyperspectral and Visible Image Fusion and Processing -- 4.1.1 Late Fusion Models -- 4.1.2 Early Fusion Models -- 4.1.3 Review of Models on the Timeline -- 4.2 Computer Vision Methods -- 4.2.1 Classification -- 4.2.2 Detection -- 4.2.3 Segmentation -- 4.3 Computer Vision Datasets -- 4.3.1 TrashNet -- 4.3.2 Wade-AI -- 4.3.3 GINI -- 4.3.4 Waste on the Street -- 4.3.5 WaDaBa -- 4.3.6 Open Litter Map -- 4.3.7 Dataset for Multilayer Hybrid Deep Learning Method -- 4.3.8 Dataset for Intelligent Urban Management System -- 4.3.9 Dataset for Automatic Garbage Detection System -- 4.3.10 Cigarette Butt Dataset -- 4.3.11 Dataset for Municipal Solid Waste Segregation -- 4.3.12 Waste Pictures Dataset -- 4.3.13 Trash-ICRA19 -- 4.3.14 DeepSeaWaste -- 4.3.15 Dataset for Garbage Detection in Video Streams -- 4.3.16 Drinking Waste -- 4.3.17 TACO -- 4.3.18 MJU-Waste -- 4.3.19 TrashCan -- 4.3.20 Aquatrash -- 4.3.21 Waste Images from Sushi Restaurant -- 4.3.22 Dataset by Mostafa Mohamed -- 4.3.23 Nonbiodegradable and Biodegradable Material Dataset -- 4.3.24 UAVVaste -- 4.3.25 FloW -- 4.3.26 Domestic Trash -- 4.3.27 Waste Segregation Dataset -- 4.3.28 Dataset for Garbage Detection. ; 4.3.29 Garbage Bag Synthetic Dataset.
• Titolo autorizzato: Scanning Technologies for Autonomous Systems
• ISBN: 3-031-59531-9
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9910874690303321
• Lo trovi qui: Univ. Federico II