Scanning Technologies for Autonomous Systems

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
				4.3.30 ZeroWaste
				4.3.31 Dataset by Sashaank Sekar
				4.3.32 TrashBox
				4.3.33 Bottle Labels Dataset
				4.3.34 Dataset for Medical Waste Sorting
				4.3.35 SynthWaste
				4.3.36 Mixed Waste Dataset
				4.3.37 Aerial Beach Waste Dataset
				4.3.38 Construction and Demolition Waste Object Detection Dataset
			4.4 Image Augmentation
		5 Conclusion
		References
	Developing a Hardware and Software Complex for Measuring the Three-Dimensional Ice Geometry on Object Surfaces
		1 Introduction
		2 Overview of the Subject Area
			2.1 Existing Methods of Measuring Three-Dimensional Geometry of Ice
			2.2 Comparative Analysis of Optical Measuring Technologies for Solving the Problem of Controlling the Three-Dimensional Geometry of Ice in a Climatic Wind Tunnel
		3 Goals and Objectives
		4 Development of a Measurement System Using Phase Triangulation of Three-Dimensional Geometry of Ice in a Climatic Wind Tunnel
			4.1 Adaptation of the Phase Triangulation Method for Measurements Under Refractive Optical Signals
			4.2 Developing a Software Module for Optimizing the Parameters of the Measuring System
			4.3 Development of a Software Module for Analyzing the Object Icing Based on the Results of Measuring Its Three-Dimensional Geometry
			4.4 Assembling the Measuring System
		5 Testing of the Developed Measuring System
		6 Conclusion
		References
Index