Preface Readership Acknowledgements Introduction Part I. The Context for Spatial Data Analysis: 1. Spatial data analysis: scientific and policy context 2. The nature of spatial data Part II. Spatial Data: Obtaining Data And Quality Issues: 3. Obtaining spatial data through sampling 4. Data quality: implications for spatial data analysis Part III. The Exploratory Analysis of Spatial Data: 5. Exploratory analysis of spatial data 6. Exploratory spatial data analysis: visualisation methods 7. Exploratory spatial data analysis: numerical methods Part IV. Hypothesis Testing in the Presence of Spatial Autocorrelation: 8. Hypothesis testing in the presence of spatial dependence Part V. Modeling Spatial Data: 9. Models for the statistical analysis of spatial data 10. Statistical modeling of spatial variation: descriptive modeling 11. Statistical modeling of spatial variation: explanatory modeling Appendices References Index.

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Spatial Data Analysis: Theory and Practice

The paper presents a system for automatic, geo-registered, real-time 3D reconstruction from video of urban scenes. The system collects video streams, as well as GPS and inertia measurements in order to place the reconstructed models in geo-registered coordinates. It is designed using current state of the art real-time modules for all processing steps. It employs commodity graphics hardware and standard CPU's to achieve real-time performance. We present the main considerations in designing the system and the steps of the processing pipeline. Our system extends existing algorithms to meet the robustness and variability necessary to operate out of the lab. To account for the large dynamic range of outdoor videos the processing pipeline estimates global camera gain changes in the feature tracking stage and efficiently compensates for these in stereo estimation without impacting the real-time performance. The required accuracy for many applications is achieved with a two-step stereo reconstruction process exploiting the redundancy across frames. We show results on real video sequences comprising hundreds of thousands of frames.

/pdf/detailed-real-time-urban-3d-reconstruction-from-video-67719pruej.pdf

Detailed Real-Time Urban 3D Reconstruction from Video

We present a new parametrization for point features within monocular simultaneous localization and mapping (SLAM) that permits efficient and accurate representation of uncertainty during undelayed initialization and beyond, all within the standard extended Kalman filter (EKF). The key concept is direct parametrization of the inverse depth of features relative to the camera locations from which they were first viewed, which produces measurement equations with a high degree of linearity. Importantly, our parametrization can cope with features over a huge range of depths, even those that are so far from the camera that they present little parallax during motion---maintaining sufficient representative uncertainty that these points retain the opportunity to "come in'' smoothly from infinity if the camera makes larger movements. Feature initialization is undelayed in the sense that even distant features are immediately used to improve camera motion estimates, acting initially as bearing references but not permanently labeled as such. The inverse depth parametrization remains well behaved for features at all stages of SLAM processing, but has the drawback in computational terms that each point is represented by a 6-D state vector as opposed to the standard three of a Euclidean XYZ representation. We show that once the depth estimate of a feature is sufficiently accurate, its representation can safely be converted to the Euclidean XYZ form, and propose a linearity index that allows automatic detection and conversion to maintain maximum efficiency---only low parallax features need be maintained in inverse depth form for long periods. We present a real-time implementation at 30 Hz, where the parametrization is validated in a fully automatic 3-D SLAM system featuring a handheld single camera with no additional sensing. Experiments show robust operation in challenging indoor and outdoor environments with a very large ranges of scene depth, varied motion, and also real time 360deg loop closing.

/pdf/inverse-depth-parametrization-for-monocular-slam-3u4x6882fg.pdf

Inverse Depth Parametrization for Monocular SLAM

Using known camera motion to estimate depth from image sequences is an important problem in robot vision. Many applications of depth-from-motion, including navigation and manipulation, require algorithms that can estimate depth in an on-line, incremental fashion. This requires a representation that records the uncertainty in depth estimates and a mechanism that integrates new measurements with existing depth estimates to reduce the uncertainty over time. Kalman filtering provides this mechanism. Previous applications of Kalman filtering to depth-from-motion have been limited to estimating depth at the location of a sparse set of features. In this paper, we introduce a new, pixel-based (iconic) algorithm that estimates depth and depth uncertainty at each pixel and incrementally refines these estimates over time. We describe the algorithm and contrast its formulation and performance to that of a feature-based Kalman filtering algorithm. We compare the performance of the two approaches by analyzing their theoretical convergence rates, by conducting quantitative experiments with images of a flat poster, and by conducting qualitative experiments with images of a realistic outdoor-scene model. The results show that the new method is an effective way to extract depth from lateral camera translations. This approach can be extended to incorporate general motion and to integrate other sources of information, such as stereo. The algorithms we have developed, which combine Kalman filtering with iconic descriptions of depth, therefore can serve as a useful and general framework for low-level dynamic vision.

/pdf/kalman-filter-based-algorithms-for-estimating-depth-from-43s98uzg19.pdf

Kalman Filter-based Algorithms for Estimating Depth from Image Sequences

The importance of landscape and heritage recording and documentation with optical remote sensing sensors is well recognized at international level. The continuous development of new sensors, data capture methodologies and multi-resolution 3D representations, contributes significantly to the digital 3D documentation, mapping, conservation and representation of landscapes and heritages and to the growth of research in this field. This article reviews the actual optical 3D measurement sensors and 3D modeling techniques, with their limitations and potentialities, requirements and specifications. Examples of 3D surveying and modeling of heritage sites and objects are also shown throughout the paper.

https://www.mdpi.com/2072-4292/3/6/1104/pdf

Heritage Recording and 3D Modeling with Photogrammetry and 3D Scanning

This book is designed to give the reader a strong grounding in the mathematical basis of photogrammetry while introducing them to the related fields, such as remote sensing and digital image processing, which are increasingly important to photogrammetric research and practice.

Introduction to modern photogrammetry

Observations and Least Squares

Manual of photogrammetry

Analysis and adjustment of survey measurements

I Concepts 1 Surveying and Mapping 2 Survey Measurements and Adjustments 3 Planning and Data Acquisition II Basic Survey Measurements 4 Distance Measurement 5 Vertical Distance Measurement Leveling 6 Angle and Direction Measurements 7 Combined Distance and Angle Measurement System III Survey Operations 8 Traverse 9 Standard Methods for Horizontal Positioning 10 Astronomy IV Modern Surveying and Mapping 11 Map Projections 12 Global Positioning System (GPS) 13 Photogrammetric Surveying and Mapping 14 Mapping, Digital Mapping, and Spatial Information Systems V Types of Surveys 15 Control and Topographic Surveying 16 Route Surveying 17 Construction Surveying 18 Land Surveys Appendixes A Elementary Mathematical Concepts B Introduction to Vector and Matrix Algebra C Coordinate Transformations (including transformations between geodetic reference systems) D Introduction to Probability and Statistics E Trigonometric Formulas and Statistical Tables

Edward M. Mikhail

Papers

Introduction to modern photogrammetry

Observations and Least Squares

Manual of photogrammetry

Analysis and adjustment of survey measurements

Surveying: Theory and Practice