Introduction to visual SLAM : from theory to practice / / Xiang Gao, Tao Zhang

• Record Nr.: UNISA996464504303316
• Autore: Gao Xiang, L.L.M.
• Pubbl/distr/stampa: Singapore : , : Springer, , [2022]; ©2022
• ISBN: 981-16-4939-1
• Descrizione fisica: 1 online resource (386 pages)
• Disciplina: 621.38456
• Soggetti: Wireless localization; Computer vision; Sensor networks
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Nota di contenuto: Intro -- Preface -- What is This Book Talking About? -- How to Use This Book? -- Source Code -- Targeted Readers -- Style -- Exercises (Self-test Questions) -- Acknowledgments -- Contents -- Part I Fundamental Knowledge -- 1 Introduction to SLAM -- 1.1 Meet ``Little Carrot'' -- 1.1.1 Monocular Camera -- 1.1.2 Stereo Cameras and RGB-D Cameras -- 1.2 Classical Visual SLAM Framework -- 1.2.1 Visual Odometry -- 1.2.2 Backend Optimization -- 1.2.3 Loop Closing -- 1.2.4 Mapping -- 1.3 Mathematical Formulation of SLAM Problems -- 1.4 Practice: Basics -- 1.4.1 Installing Linux -- 1.4.2 Hello SLAM -- 1.4.3 Use CMake -- 1.4.4 Use Libraries -- 1.4.5 Use IDE -- 2 3D Rigid Body Motion -- 2.1 Rotation Matrix -- 2.1.1 Points, Vectors, and Coordinate Systems -- 2.1.2 Euclidean Transforms Between Coordinate Systems -- 2.1.3 Transform Matrix and Homogeneous Coordinates -- 2.2 Practice: Use Eigen -- 2.3 Rotation Vectors and the Euler Angles -- 2.3.1 Rotation Vectors -- 2.3.2 Euler Angles -- 2.4 Quaternions -- 2.4.1 Quaternion Operations -- 2.4.2 Use Quaternion to Represent a Rotation -- 2.4.3 Conversion of Quaternions to Other Rotation Representations -- 2.5 Affine and Projective Transformation -- 2.6 Practice: Eigen Geometry Module -- 2.6.1 Data Structure of the Eigen Geometry Module -- 2.6.2 Coordinate Transformation Example -- 2.7 Visualization Demo -- 2.7.1 Plotting Trajectory -- 2.7.2 Displaying ; Camera Pose -- 3 Lie Group and Lie Algebra -- 3.1 Basics of Lie Group and Lie Algebra -- 3.1.1 Group -- 3.1.2 Introduction of the Lie Algebra -- 3.1.3 The Definition of Lie Algebra -- 3.1.4 Lie Algebra mathfrakso(3) -- 3.1.5 Lie Algebra mathfrakse(3) -- 3.2 Exponential and Logarithmic Mapping -- 3.2.1 Exponential Map of SO(3) -- 3.2.2 Exponential Map of SE(3) -- 3.3 Lie Algebra Derivation and Perturbation Model -- 3.3.1 BCH Formula and Its Approximation.; 3.3.2 Derivative on SO(3) -- 3.3.3 Derivative Model -- 3.3.4 Perturbation Model -- 3.3.5 Derivative on SE(3) -- 3.4 Practice: Sophus -- 3.4.1 Basic Usage of Sophus -- 3.4.2 Example: Evaluating the Trajectory -- 3.5 Similar Transform Group and Its Lie Algebra -- 3.6 Summary -- 4 Cameras and Images -- 4.1 Pinhole Camera Models -- 4.1.1 Pinhole Camera Geometry -- 4.1.2 Distortion -- 4.1.3 Stereo Cameras -- 4.1.4 RGB-D Cameras -- 4.2 Images -- 4.3 Practice: Images in Computer Vision -- 4.3.1 Basic Usage of OpenCV -- 4.3.2 Basic OpenCV Images Operations -- 4.3.3 Image Undistortion -- 4.4 Practice: 3D Vision -- 4.4.1 Stereo Vision -- 4.4.2 RGB-D Vision -- 5 Nonlinear Optimization -- 5.1 State Estimation -- 5.1.1 From Batch State Estimation to Least-Square -- 5.1.2 Introduction to Least-Squares -- 5.1.3 Example: Batch State Estimation -- 5.2 Nonlinear Least-Square Problem -- 5.2.1 The First and Second-Order Method -- 5.2.2 The Gauss-Newton Method -- 5.2.3 The Levernberg-Marquatdt Method -- 5.2.4 Conclusion -- 5.3 Practice: Curve Fitting -- 5.3.1 Curve Fitting with Gauss-Newton -- 5.3.2 Curve Fitting with Google Ceres -- 5.3.3 Curve Fitting with g2o -- 5.4 Summary -- Part II SLAM Technologies -- 6 Visual Odometry: Part I -- 6.1 Feature Method -- 6.1.1 ORB Feature -- 6.1.2 Feature Matching -- 6.2 Practice: Feature Extraction and Matching -- 6.2.1 ORB Features in OpenCV -- 6.2.2 ORB Features from Scratch -- 6.2.3 Calculate the Camera Motion -- 6.3 2D-2D: Epipolar Geometry -- 6.3.1 Epipolar Constraints -- 6.3.2 Essential Matrix -- 6.3.3 Homography -- 6.4 Practice: Solving Camera Motion with Epipolar Constraints -- 6.4.1 Discussion -- 6.5 Triangulation -- 6.6 Practice: Triangulation -- 6.6.1 Triangulation with OpenCV -- 6.6.2 Discussion -- 6.7 3D-2D PnP -- 6.7.1 Direct Linear Transformation -- 6.7.2 P3P -- 6.7.3 Solve PnP by Minimizing the Reprojection Error.; 6.8 Practice: Solving PnP -- 6.8.1 Use EPnP to Solve the Pose -- 6.8.2 Pose Estimation from Scratch -- 6.8.3 Optimization by g2o -- 6.9 3D-3D Iterative Closest Point (ICP) -- 6.9.1 Using Linear Algebra (SVD) -- 6.9.2 Using Non-linear Optimization -- 6.10 Practice: Solving ICP -- 6.10.1 Using SVD -- 6.10.2 Using Non-linear Optimization -- 6.11 Summary -- 7 Visual Odometry: Part II -- 7.1 The Motivation of the Direct Method -- 7.2 2D Optical Flow -- 7.2.1 Lucas-Kanade Optical Flow -- 7.3 Practice: LK Optical Flow -- 7.3.1 LK Flow in OpenCV -- 7.3.2 Optical Flow with Gauss-Newton Method -- 7.3.3 Summary of the Optical Flow Practice -- 7.4 Direct Method -- 7.4.1 Derivation of the Direct Method -- 7.4.2 Discussion of Direct Method -- 7.5 Practice: Direct method -- 7.5.1 Single-Layer Direct Method -- 7.5.2 Multi-layer Direct Method -- 7.5.3 Discussion -- 7.5.4 Advantages and Disadvantages of the Direct Method -- 8 Filters and Optimization Approaches: Part I -- 8.1 Introduction -- 8.1.1 State Estimation from Probabilistic Perspective -- 8.1.2 Linear Systems and the Kalman Filter -- 8.1.3 Nonlinear Systems and the EKF -- 8.1.4 Discussion About KF and EKF -- 8.2 Bundle Adjustment and Graph Optimization -- 8.2.1 The Projection Model and Cost Function -- 8.2.2 Solving Bundle Adjustment -- 8.2.3 Sparsity -- 8.2.4 Minimal Example of BA -- 8.2.5 Schur Trick -- 8.2.6 Robust Kernels -- 8.2.7 Summary -- 8.3 Practice: BA with Ceres -- 8.3.1 BAL Dataset -- 8.3.2 Solving BA in Ceres -- 8.4 Practice: BA with g2o -- 8.5 Summary -- 9 Filters and Optimization ; Approaches: Part II -- 9.1 Sliding Window Filter and Optimization -- 9.1.1 Controlling the Structure of BA -- 9.1.2 Sliding Window -- 9.2 Pose Graph Optimization -- 9.2.1 Definition of Pose Graph -- 9.2.2 Residuals and Jacobians -- 9.3 Practice: Pose Graph -- 9.3.1 Pose Graph Using g2o Built-in Classes.; 9.3.2 Pose Graph Using Sophus -- 9.4 Summary -- 10 Loop Closure -- 10.1 Loop Closure and Detection -- 10.1.1 Why Loop Closure Is Needed -- 10.1.2 How to Close the Loops -- 10.1.3 Precision and Recall -- 10.2 Bag of Words -- 10.3 Train the Dictionary -- 10.3.1 The Structure of Dictionary -- 10.3.2 Practice: Creating the Dictionary -- 10.4 Calculate the Similarity -- 10.4.1 Theoretical Part -- 10.4.2 Practice Part -- 10.5 Discussion About the Experiment -- 10.5.1 Increasing the Dictionary Scale -- 10.5.2 Similarity Score Processing -- 10.5.3 Processing the Keyframes -- 10.5.4 Validation of the Detected Loops -- 10.5.5 Relationship with Machine Learning -- 11 Dense Reconstruction -- 11.1 Brief Introduction -- 11.2 Monocular Dense Reconstruction -- 11.2.1 Stereo Vision -- 11.2.2 Epipolar Line Search and Block Matching -- 11.2.3 Gaussian Depth Filters -- 11.3 Practice: Monocular Dense Reconstruction -- 11.3.1 Discussion -- 11.3.2 Pixel Gradients -- 11.3.3 Inverse Depth Filter -- 11.3.4 Pre-Transform the Image -- 11.3.5 Parallel Computing -- 11.3.6 Other Improvements -- 11.4 Dense RGB-D Mapping -- 11.4.1 Practice: RGB-D Point Cloud Mapping -- 11.4.2 Building Meshes from Point Cloud -- 11.4.3 Octo-Mapping -- 11.4.4 Practice: Octo-mapping -- 11.5 *TSDF and RGB-D Fusion Series -- 11.6 Summary -- 12 Practice: Stereo Visual Odometry -- 12.1 Why Do We Have a Separate Engineering Chapter? -- 12.2 Framework -- 12.2.1 Data Structure -- 12.2.2 Pipeline -- 12.3 Implementation -- 12.3.1 Implement the Basic Data Structure -- 12.3.2 Implement the Frontend -- 12.3.3 Implement the Backend -- 12.4 Experiment Results -- 13 Discussions and Outlook -- 13.1 Open-Source Implementations -- 13.1.1 MonoSLAM -- 13.1.2 PTAM -- 13.1.3 ORB-SLAM Series -- 13.1.4 LSD-SLAM -- 13.1.5 SVO -- 13.1.6 RTAB-MAP -- 13.1.7 Others -- 13.2 SLAM in Future -- 13.2.1 IMU Integrated VSLAM.; 13.2.2 Semantic SLAM -- Appendix A Gaussian Distribution -- A.1 Gaussian Distribution -- A.2 Transform of Gaussian Variables -- A.2.1 Linear Transform -- A.2.2 Normalized Product -- A.2.3 Joint and Conditional Distribution -- A.3 Example of Joint Distribution -- Appendix B Matrix Derivatives -- B.1 Scalar Function with Vector Variable -- B.2 Vector Function with Vector Variable -- Appendix References.

From a virgin womb [[electronic resource] ] : the Apocalypse of Adam and the virgin birth / / by Andrew J. Welburn

• Record Nr.: UNINA9910782317403321
• Autore: Welburn Andrew J
• Pubbl/distr/stampa: Leiden ; ; Boston, : Brill, 2008
• ISBN: 1-281-93702-9; 9786611937027; 90-474-2357-7
• Descrizione fisica: 1 online resource (233 p.)
• Collana: Biblical interpretation series, , 0928-0731 ; ; v. 91
• Disciplina: 232.92/1
• Soggetti: Virgin birth
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Note generali: Description based upon print version of record.
• Nota di bibliografia: Includes bibliographical references (p. [211]-217) and index.
• Nota di contenuto: Preliminary Materials / A.J. Welburn -- Introduction. From The Virgin Birth To The Gospel Of Matthew / A.J. Welburn -- Chapter One. Adam’s Apocalypse: Cg V/5 As Testament And Jewish Revelation / A.J. Welburn -- Chapter Two. Biblical Materials: Exile And Return / A.J. Welburn -- Chapter Three. \'Syncretistic\' Materials / A.J. Welburn -- Chapter Four. An Unnatural Birth (Mt. 1,18–21 And Cg V 78,6–17) / A.J. Welburn -- Chapter Five. A Virgin Birth And A Persecuted Child (Mt. 1–2 And Cg V 78,18–26) / A.J. Welburn -- Chapter Six. The Magi In Bethlehem And The Queen Of The South (Mt. 2,1–12 And Cg V 78,27 – 79,19) / A.J. Welburn -- Conclusion. The Virgin Birth: Some Reflections On Its Meaning / A.J. Welburn -- Appendix. The Zarathuštra-Legend And Cg V/5 77,26 – 78,26 / A.J. Welburn -- List Of Abbreviations / A.J. Welburn -- Bibliography / A.J. Welburn -- Index Of Principal Names And Subjects / A.J. Welburn -- Index Of Modern Authors / A.J. Welburn -- Index Of Ancient Sources / A.J. Welburn.
• Sommario/riassunto: Scholarly researches on the virgin birth have often focussed rather narrowly on the theological and historical difficulties it tends to raise. The Nag Hammadi Apocalypse of Adam, however, provides for the first time a glimpse into the wider background of ideas and myths to which it belonged. Prophecies there concerning a universal 'Illuminator' mention his birth 'from a virgin womb'. Several of the stories, drawn from Iranian and other sources , also appear in apocalyptic and ; testamental literature contemporary with Christian origins. The book centrally analyses a body of extraordinarily detailed narrative parallels between a cluster of stories in the Apocalypse and the infancy narratives of Mt. 1-2, concluding that these stories serve to identify Jesus as the True Prophet who is the fulfilment of history - though not as Son of God. The question of Mt.'s special tradition and its relation to Lk. is also cast in a new light.