Raytheon Company

Jody Dale Verret

Stephen J Raif

Richard W Ely

Corey J Collard

Grant B Boroughs

Steven B Seida

Wyatt D Sharp, Iii

John J Coogan

Torsten Staab

Joseph C Landry

James N Head

Matthew A Schaefer

Jeffrey S Klein

Jacob Wesely Gallaway

Jeremy Jens Gerhart

Carlton E Nance

Jerry O Moore

Robert T Pierce

Erich Hefner

Paul L Pryor

Ali J Arik

James G Hayes

Kevin M Grange

Kathryn A Welin

Robert V Opalecky

Wenkai Qin

Robert M Willer

Mark J Graebner

Systems, devices, methods, and computer-readable media for determining planarity in a 3D data set are provided. A method can include receiving or retrieving three-dimensional (3D) data of a geographical region, dividing the 3D data into first contiguous regions of specified first geographical dimensions, determining, for each first contiguous region of the first contiguous regions, respective measures of variation, identifying, based on the respective measures of variation, a search radius, dividing the 3D data into respective second contiguous or overlapping regions with dimensions the size of the identified search radius, and determining, based on the identified search radius, a planarity of each of the respective second contiguous or overlapping regions.

Determining minimum region for finding planar surfaces

Subject matter regards generating a 3D point cloud and registering the 3D point cloud to the surface of the Earth (sometimes called 'geo-locating'). A method can include capturing, by unmanned vehicles (UVs), image data representative of respective overlapping subsections of the object, registering the overlapping subsections to each other, and geo-locating the registered overlapping subsections.

Collaborative 3D mapping and surface registration

A system first registers a two-dimensional image to targetable three-dimensional data. A user or automated process selects image coordinates of a target within the registered two-dimensional image. The system intersects the image coordinates of the target with the targetable three-dimensional data, thereby generating geodetic coordinates of the target in a point cloud. Error estimates for the geodetic coordinates of the target are generated, and the system stores the geodetic coordinates and associated error of the target in a database for use in downstream exploitation.

Three-dimensional-enabled targeting of imagery with rigorous error propagation

A system identifies strong features in conjugate digital images and correlates the conjugate digital images for an identification of candidate tie points using only portions of the conjugate digital images that include the strong features.

Fast determinant of Hessian filtering for image tiepoint candidate area assessment

Systems, devices, methods, and computer-readable media for horizon-based navigation. A method can include receiving image data corresponding to a geographical region in a field of view of an imaging unit and in which the device is situated, based on the received image data, generating, by the processing unit, an image horizon corresponding to a horizon of the geographical region and from a perspective of the imaging unit, projecting three-dimensional (3D) points of a 3D point set of the geographical region to an image space of the received image data resulting in a synthetic image, generating, by the processing unit, a synthetic image horizon of the synthetic image, and responsive to determining the image horizon sufficiently correlates with the synthetic image horizon, providing a location corresponding to a perspective of the synthetic image as a location of the processing unit.

Horizon-based navigation

A method can include identifying a geolocation of an object in an image, the method comprising receiving data indicating a pixel coordinate of the image selected by a user, identifying a data point in a targetable three-dimensional (3D) data set corresponding to the selected pixel coordinate, and providing a 3D location of the identified data point.

Image targeting via targetable 3D data

Discussed herein are devices, systems, and methods for multi-image ground control point (GCP) determination. A method can include extracting, from a first image including image data of a first geographical region, a first image template, the first image template including a contiguous subset of pixels from the first image and a first pixel of the first image indicated by the GCP, predicting a first pixel location of the GCP in a second image, the second image including image data of a second geographical overlapping with the first geographical region, extracting, from the second image, a second image template, the second image template including a contiguous subset of pixels from the second image and a second pixel corresponding to the pixel location, identifying a second pixel of the second image corresponding to a highest correlation score, and adding a second pixel location of the identified pixel to the GCP.

Automatic multi-image 3D ground control point extraction

Discussed herein are devices, systems, and methods for merging point cloud data with error propagation. A can include reducing a sum aggregate of discrepancies between respective tie points and associated 3D points in first and second 3D images, adjusting 3D error models of the first and second 3D images based on the reduced discrepancies to generate registered 3D images, and propagating an error of the first or second 3D images to the registered 3D image to generate error of the registered 3D images.

Point cloud registration with error propagation

A process improves accuracy in mapping geodetic coordinates to image sensor coordinates via an image rational function. The image rational function is fitted to an image-to ground sensor model at an input grid of image u coordinates and image v coordinates, and further at geodetic longitudes, geodetic latitudes, and geodetic heights corresponding to the image u coordinates and the image v coordinates. This fitting generates fit residuals. The fit residuals are stored as a function of the image u coordinates, the image v coordinates, and the geodetic height coordinates. The fit residuals are applied to metadata associated with the image rational function. This application corrects for a residual error in a fit of the image rational function to the image-to-ground sensor model.

Geolocation improvement of image rational functions via a fit residual correction

A system receives digital images of a geographic location, associates each digital image with ground control points in a set of reference stereo images, and associates each digital image to each other digital image via image to image tiepoints. The system updates a geometry of each image via a bundle adjustment, and uses a prioritized stacking order to establish piecewise linear seam lines between each of the images. The system finally builds a prioritized map in a mosaic space specifying the source image pixels that are used in each region of the output mosaic, and forms the mosaic image using the prioritized map.

Ortho-image mosaic production system

An image processing technique uses requirements for a geospatial distribution of image tie points for a triangulation of images. The images are correlated, thereby generating candidate tie points across the images. Statistical consistency checks are applied to the images to identify and dispose of the candidate tie points that are local outliers, and a geometric identification technique is applied to the images to identify and dispose of the candidate tie points that are global outliers. The candidate tie points that are not local outliers or global outliers are spatially down-selected such that the spatially down-selected candidate tie points satisfy the one or more requirements for the geospatial distribution.

Image tiepoints

A computer vision method, executed by one or more processors, for generating a single 3D model view of a geographic scene includes: receiving image data for the scene from a plurality of sensors located at different angles with respect to the geographic scene; dividing the image data into a plurality of image spatial regions; correlating the image data in each image spatial region to obtain a score for each image data in each image spatial region; grouping the image data in each image spatial region into two or more image clusters, based on the scores for each image; performing a multi-ray intersection within each image cluster to obtain a 3D reference point for each region; for each region, combining the one or more clusters, based on the 3D reference point for the region; and registering the combined clusters for each region to obtain a single 3D model view of the scene.

3D view model generation of an object utilizing geometrically diverse image clusters

A system and a method for processing multi-linear image data by measuring a relative oscillatory motion from a first-imaged array of the multi-linear optical array to a second-imaged array of the multi-linear optical array as a first function in time domain via image correlation; transforming the first function from the time domain to a second function in frequency domain; converting real and the imaginary parts of the second function to polar coordinates to generate a magnitude and a phase; correcting the polar coordinates from the second function in the frequency domain to generate a third function; converting the third function to rectangular coordinates to generate a fourth function in the frequency domain; and transforming the fourth function from the frequency domain to a fifth function in the time domain.

Oscillatory motion compensation in frequency domain in image motion sensing systems

A system and method for displaying a three-dimensional surface along with ellipsoids representing covariances. In one embodiment, at a point on a three dimensional surface, an ellipsoid is formed having principal axes proportional to the eigenvalues of a covariance matrix. The ellipsoid and the three-dimensional surface are projected onto a two-dimensional plane for display on a two-dimensional screen to a user. The covariance matrix may be an estimated error covariance or a sample covariance.

Methods and processes for interactive display of three dimensional covariance

A system and method of generating point clouds from passive images. Image clusters are formed, wherein each image cluster includes two or more passive images selected from a set of passive images. Quality of the point cloud that could be generated from each image cluster is predicted for each image cluster based on a performance prediction score for each image cluster. A subset of image clusters is selected for further processing based on their performance prediction scores. A mission-specific quality score is determined for each point cloud generated and the point cloud with the highest quality score is selected for storage.

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Rockwall, TX, United States of America

Jody Dale Verret