Position, Navigation, and Timing Technologies in the 21st Century

14 Chapter 46bFigure 46.38 Segmented estimator with updates.Figure 46.39 From raw inertial instrument data to final navigation outputs....Figure 46.40 Measurement geometry over time.Figure 46.41 Carrier‐phase residuals of the next‐to‐last flight segment.

15 Chapter 47Figure 47.1 The basic components of an AFR (clock) are the collection of ato...Figure 47.2 Illustration of the concepts of accuracy and stability. The plot...Figure 47.3a Energy level diagram of Rb showing the lowest energy quantum st...Figure 47.3b When a magnetic field is applied to 87Rb atoms, the two hyperfi...Figure 47.3c Illustration of the optical absorption spectra of Rb on the res...Figure 47.4 Basic diagram of a lamp‐pumped Rb AFR, consisting of three Rb va...Figure 47.5 On top is an image of an? historic Rb AFR that was produced coll...Figure 47.6 Representative frequency instability of GPS Rb AFRs, Blocks I to...Figure 47.7 Representative diagram of Cs beam atomic frequency standard. Thi...Figure 47.8 Detected atom flux measured at the hot wire (often platinum) plu...Figure 47.9 Illustrative diagram of the design and internal structure of an ...Figure 47.10 Image of a passive hydrogen maser (PHM) used on GALILEO. ESA ph...Figure 47.11 Plot showing the expected frequency instability of some advance...Figure 47.12 Simplified diagram of the concept of a CSAC, here using the Cs ...Figure 47.13 On the left is an image of an early realization of a CSAC physi...

16 Chapter 48Figure 48.1 Temporal variations amplitude versus frequency (Marshall [11])....Figure 48.2 Twenty‐five days of temporal variations recorded At Boulder, Col...Figure 48.3 Compensation of magnetometer data to remove aircraft effects (Re...Figure 48.4 Examples of three‐axis magnetic field measurements in three near...Figure 48.5 Typical example of magnetic field variation in a university hall...Figure 48.6 Likelihood function value as a function of position for example ...Figure 48.7 Position error from hallway magnetic field positioning test. Y‐a...Figure 48.8 Example set of likelihoods at a single epoch.Figure 48.9 Maps of magnetic field navigation test routes (Images from Googl...Figure 48.10 AFIT route test results.Figure 48.11 Neighborhood route test results.Figure 48.12 Large route test results.Figure 48.13 Power spectral density of temporal variation and crustal field....Figure 48.14 Difference Between the 2012 magnetic anomaly map and the 2015 m...Figure 48.15 2012 Magnetic anomaly map over Louisa, Virginia, and 2015 fligh...Figure 48.16 Difference between the expected measurements from interpolation...Figure 48.17 North and east error over 1 h segment of flight profile.Figure 48.18 Filter estimation of temporal variations.

17 Chapter 49Figure 49.1 Example of a 2D point cloud from a Hokuyo UTM‐30LX laser range s...Figure 49.2 Example of a 3D point cloud from a Velodyne HDL‐64E multi‐apertu...Figure 49.3 Example of a 3D point cloud from a structured light 3D imager (O...Figure 49.4 Laser/inertial integration example using a complementary Kalman ...Figure 49.5 Line extraction example: the split‐and‐merge method.Figure 49.6 Split‐and‐merge line extraction results using SICK‐360 van test ...Figure 49.7 Calculation of the shortest point to the line for each point (So...Figure 49.8 Line extraction example with line segments and associated standa...Figure 49.9 Two‐dimensional (2D) feature‐based laser navigation using line f...Figure 49.10 Two‐dimensional (2D) feature‐based laser navigation using line ...Figure 49.11 Two‐dimensional (2D) feature‐based laser navigation using line ...Figure 49.12 Extraction of the two‐dimensional (2D) algorithm to three dimen...Figure 49.13 Feature‐based laser/inertial integration.Figure 49.14 Complementary Kalman filter (CKF) for inertial error estimation...Figure 49.15 Basic principle of feature‐based SLAM.Figure 49.16 Feature‐based EKF_SLAM algorithm.Figure 49.17 Feature‐based SLAM data association (Bailey [18]).Figure 49.18 EKF‐SLAM (yellow) versus GPS (blue).Figure 49.19 FastSLAM mechanization.Figure 49.20 Front‐end and back‐end processing for graph‐based SLAM methods....Figure 49.21 Example of a factor graph used for offline processing of data u...Figure 49.22 Example of using iterative closest point (ICP) on actual point ...Figure 49.23 Complementary Kalman filter (CKF) for inertial error estimation...Figure 49.24 Map lookup function (Vadlamani and Uijt de Haag [44]).Figure 49.25 Gradient‐based search method to find the lateral error offset (...Figure 49.26 Airborne laser‐scanner system (ALS)‐based terrain navigator usi...Figure 49.27 Dual ALS (DALS)‐based terrain navigator without a known terrain...Figure 49.28 (a) Feedforward and (b) feedback coupled dual airborne laser‐sc...Figure 49.29 Simulation results for the dual airborne laser‐scanner system (...Figure 49.30 Dual airborne laser‐scanner system/inertial navigation system (...Figure 49.31 Basic principle of forming an occupancy grid (gray: misses, bla...Figure 49.32 Example of an occupancy grid with a superimposed aerial robot t...Figure 49.33 Pose estimation based on matching the laser scan against availa...Figure 49.34 FastSLAM method using occupancy grids instead of features.Figure 49.35 (a) GridSLAM map and trajectory results, (b) trajectories of al...Figure 49.36 Small unmanned aircraft system (sUAS) mapping results for Ohio ...Figure 49.37 sUAS mapping results for Ohio University Stocker Center third f...

18 Chapter 50Figure 50.1 Simple imaging system model. The imaging system transforms the s...Figure 50.2 Camera frame definition.Figure 50.3 Commonly used camera pinhole model.Figure 50.4 Mapping from 3D camera coordinates to 2D normalized coordinates,...Figure 50.5 Image plane for a n x× n yimage, showing the relationship betwee...Figure 50.6 Relationship between the camera frame (and virtual image plane),...Figure 50.7 Sample feature extraction. In this image, notional features are ...Figure 50.8 Harris corner extraction example image.Figure 50.9 Harris corner edge response function.Figure 50.10 Harris corner metric sample results.Figure 50.11 Sample line extraction. In this image, lines are detected using...Figure 50.12 Frequency response of the Gaussian blur filter for varying blur...Figure 50.13 Impulse response of the difference of the Gaussian filter.Figure 50.14 Frequency response of the difference of the Gaussian filter. Th...Figure 50.15 Sample image of airfield.Figure 50.16 Sample image scale decomposition. As the filter center frequenc...Figure 50.17 Sample 12‐Segment FAST Feature Detection Nucleus. The center pi...Figure 50.18 Sample feature matching exercise. A feature descriptor from Fra...Figure 50.19 Sample unconstrained correspondence. In this case, a correspond...Figure 50.20 Stochastic feature prediction. Optical features of interest are...Figure 50.21 Epipolar geometry.Figure 50.22 Epipolar geometry for a landmark of interest.Figure 50.23 Two‐view geometry navigation processing example.Figure 50.24 Comparison of PnP error distribution between 6DOF (position and...Figure 50.25 Example of monocular imaging scale ambiguity. In this figure, t...Figure 50.26 Example of stereoscopic ranging. The depth of landmarks “A” and...Figure 50.27 Example of forced perspective imaging. In this photograph, the ...Figure 50.28 Example of automated attitude stabilization by tracking paralle...Figure 50.29 Overview of image‐aided inertial algorithm. Inertial measuremen...Figure 50.30 Comparison of image‐aided inertial navigation solutions for ind...

19 Chapter 51Figure 51.1 Evolution of photogrammetric equipment: (a) early large‐format a...Figure 51.2 Georeferencing/navigation concepts.Figure 51.3 High‐resolution CCD sensor with main parametersFigure 51.4 Linear‐sensor‐based high‐resolution multispectral camera by Leic...Figure 51.5 Examples of image degradation.Figure 51.6 Geometric model of the pinhole camera.Figure 51.7 Coordinate systems in photogrammetry.Figure 51.8 Pixel and photo‐coordinate system.Figure 51.9 Interior parameters.Figure 51.10 Barrel (a) and pincushion (b) distortions.Figure 51.11 Image (a) taken with wide‐angle optics and (b) after distortion...Figure 51.12 Relationship between the image and object space.Figure 51.13 Classical airborne case of stereo photogrammetry.Figure 51.14 Epipolar constraints.Figure 51.15 Overview of the typical photogrammetric processing workflow.Figure 51.16 Tie and ground control points (GCPs) in aerial photogrammetry....Figure 51.17 Calibration targets.Figure 51.18 Generated tie points from a UAS image.Figure 51.19 Simple bundle adjustment example (Triggs et al. [32]).Figure 51.20 Result of the bundle adjustment: georeferenced image planes and...Figure 51.21 Epipolar resampling.Figure 51.22 Left image, right image and disparity.Figure 51.23 Orthorectification.Figure 51.24 Examples of close‐range and indoor photogrammetric applications...Figure 51.25 Parameters of the aerial flight planning.Figure 51.26 Rotary‐ and fixed‐wing UAVs: (a) DJI Phantom, (b) Bergen custom...Figure 51.27 Spectral bands of three satellite systems (UB – Ultra Blue, B –...