Honeywell International Inc.

Benjamin Mohr

Michael Ray Elgersma

Yunqian Ma

Scott I Snyder

Robert Compton

John B McKitterick

Vijay Venkataraman

John Burt McKitterick

Kevin Sweeney

Peter Lommel

Justin Joseph Syrstad

Christopher Jon Visker

Nicholas C Cirillo, Jr

Nicholas C Cirillo

Scott Snyder

In an embodiment, a method is provided. The method comprises extracting, from an image acquired with a first image-capture device, an image portion having dimensions of an extent of a second image-capture device. The method further comprises normalizing a parameter of the image portion. The method further comprises determining at least one edge of at least one object in the image portion; and generating, in response to the determined at least one edge, an edge map corresponding to the image portion.

Edge detection via window grid normalization

A system and methods for reducing map search area requirements in a navigation system are disclosed. The system includes a vehicle, an imaging device onboard the vehicle configured to generate an image scan, receive at least one image responsive to the image scan, and a processing device configured to receive and store the at least one image. The system further includes a navigation system onboard the vehicle configured to store an image of a map, and a learning network associated with the navigation system and configured to divide the image of the map into a plurality of map subsections, recognize each one of a plurality of images of different landmarks on the map, generate a set of classifications for each map subsection, and associate each classification of the set of classifications with at least one landmark of the different landmarks on the map.

System and methods for reducing the map search space requirements in a vision-inertial navigation system

A method of determining at least one of position and attitude in relation to an object is provided. The method includes capturing at least two images of the object with at least one camera. Each image is captured at a different position in relation to the object. The images are converted to edge images. The edge images of the object are converted into three-dimensional edge images of the object using positions of where the at least two images were captured. Overlap edge pixels in the at least two three-dimensional edge images are located to identify overlap points. A three dimensional edge candidate point image of the identified overlapped points in an evidence grid is built. The three dimensional candidate edge image in the evidence grid is compared with a model of the object to determine at least one of a then current position and attitude in relation to the object.

Position and attitude determination method and system using edge images

A method for compressing three-dimensional data points is disclosed. The method includes measuring a plurality of three-dimensional data points using one or more sensors communicatively coupled to the computing device, where each three-dimensional data point represents a point in a three-dimensional environment. The method also includes dividing the three-dimensional environment into a plurality of spatial cubes, wherein each three-dimensional data point is mapped to one spatial cube. The method also includes, for each spatial cube, converting the three-dimensional data points in the respective spatial cube to a two-dimensional plane based on a number of three-dimensional data points in the respective spatial cube. The method also includes, for each two-dimensional plane, determining polygon vertices of a planar polygon at points where an edge of an associated spatial cube intersects the respective two-dimensional plane. The method also includes sending the polygon vertices of each planar polygon to a ground station.

Compressing data points into polygons

A method of generating a map for image aided navigation comprises detecting one or more edges in one or more terrain images, and obtaining a compressed edge representation of the one or more edges. The compressed edge representation is obtained by a method comprising fitting a geometric form or other mathematical model to the one or more edges, calculating one or more coefficients based on the fitted geometric form or other mathematical model, and storing the one or more coefficients in a database for use in generating the map for image aided navigation.

Compressed edge map representation for image aided navigation

An example embodiment includes a method for initializing a navigation system. The method includes receiving inertial measurement data from an inertial measurement unit over time and storing the inertial measurement data in a buffer with an indication of a time of validity for the inertial measurement data. The method also includes receiving navigation data from an aiding source, the navigation data having a time of validity, and initializing a navigation solution with the navigation data. The method also includes summing inertial measurement data from the buffer to produce an inertial motion estimate for a time increment after the time of validity of the navigation data, applying at least one of coning or sculling compensation to the inertial motion estimate to produce a compensated inertial motion estimate, and propagating the navigation solution forward based on the compensated inertial motion estimate.

Navigation system initialization with inertial data compensation

Systems and methods for feature selection and matching are provided. In certain embodiments, a method for matching features comprises extracting a first plurality of features from current image data acquired from at least one sensor and extracting a second plurality of features from a prior map, wherein the prior map represents an environment containing the navigation system independently of data currently acquired by the at least one sensor. The method also comprises identifying at least one first feature in the first plurality of features and at least one second feature in the second plurality of features that have associated two-dimensional representations; and identifying at least one corresponding pair of features by comparing a three-dimensional representations of the at least one first feature to a three-dimensional representation of the at least one second feature.

System and methods for feature selection and matching

A method for collaborative navigation between two or more platforms is provided. The method comprises establishing a communication link between a first platform and a second platform, making a sensor measurement from the first platform, updating state and covariance elements of the first platform, and transmitting the updated state and covariance elements from the first platform to the second platform. A conditional update is performed on the second platform to compute a new estimate of state and covariance elements on the second platform, which takes into account the measurement from the first platform. The method further comprises making a sensor measurement from the second platform, updating state and covariance elements of the second platform, and transmitting the updated state and covariance elements from the second platform to the first platform. A conditional update is performed on the first platform to compute a new estimate of state and covariance elements on the first platform, which takes into account the measurement from the second platform.

Collaborative navigation using conditional updates

An inertial sensing system comprises a first multi-axis atomic inertial sensor, a second multi-axis atomic inertial sensor, and an optical multiplexer optically coupled to the first and second multi-axis atomic inertial sensors. The optical multiplexer is configured to sequentially direct light along different axes of the first and second multi-axis atomic inertial sensors. A plurality of micro-electrical-mechanical systems (MEMS) inertial sensors is in operative communication with the first and second multi-axis atomic inertial sensors. Output signals from the first and second multi-axis atomic inertial sensors aid in correcting errors produced by the MEMS inertial sensors by sequentially updating output signals from the MEMS inertial sensors.

Multi-axis atomic inertial sensor system

A method for navigation comprises constructing a current map that includes two-dimensional or three dimensional representations of an area, detecting one or more edge features on the current map, and generating a first fine-edge map based on the edge features. The method further comprises retrieving a historical map that includes two-dimensional or three dimensional representations of the area, detecting one or more edge features on the historical map, and generating a second fine-edge map based on the edge features. Thereafter, a coarse version of the current map is generated from the first fine-edge map, and a coarse version of the historical map is generated from the second fine-edge map. The coarse versions of the current and historical maps are then correlated to determine a first position and orientation. The first fine-edge map is then correlated with the second fine-edge map to determine a second, more accurate, position and orientation.

Method of correlating images with terrain elevation maps for navigation

A method for wide baseline feature matching comprises capturing one or more images from an image sensor on each of two or more platforms when the image sensors have overlapping fields of view, performing a 2-D feature extraction on each of the captured images in each platform using local 2-D image feature descriptors, and calculating 3-D feature locations on the ellipsoid of the Earth surface from the extracted features using a position and attitude of the platform and a model of the image sensor. The 3-D feature locations are updated using digital terrain elevation data (DTED) as a constraint, and the extracted features are matched using the updated 3-D feature locations to create a common feature zone. A subset of features from the common feature zone is selected, and the subset of features is inputted into a collaborative filter in each platform. A convergence test is then performed on other subsets in the common feature zone, and falsely matched features are pruned from the common feature zone.

Wide baseline feature matching using collobrative navigation and digital terrain elevation data constraints

Systems and methods to incorporate master navigation system resets during transfer alignment are provided. In one embodiment, a system comprises: a set of local inertial sensors; a local navigation processor coupled to local inertial sensors, the local navigation processor receiving inertial navigation data from local inertial sensors and converting the data into a navigation solution; a local Kalman filter (LKF) coupled to the local navigation processor and a master Kalman filter (MKF), the LKF receiving the navigation solution. The LKF receives from the MKF a Precision Transfer Alignment Message (PTAM) that includes at least one navigation aid measurement. The LKF inputs the navigation aid measurement into a measurement formation algorithm and calculates a measurement residual. The LKF receives from the MKF a Reset Transfer Alignment Message (RTAM) that includes a bias correction. The LKF inputs the bias correction into a state propagation algorithm to add to a navigation state.

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St. Louis Park, MN, United States of America

Benjamin Mohr