Honeywell International Inc.

Yunqian Ma

Michael E Bazakos

Isaac D Cohen

Vassilios Morellas

Benjamin Mohr

Michael Ray Elgersma

Jan Lukas

Ben Miller

Roland Miezianko

Andrew H Johnson

Wesley J Hawkinson

Rand P Whillock

Bruce W Anderson

John B McKitterick

Michal Dobes

Shrikant P Rao

Peter Lommel

Qian Yu

Scott McCloskey

Kwong Wing Au

Vibhor L Bageshwar

Thomas R Markham

Emray R Goossen

Jindrich Dunik

Kirk A Schloegel

John Burt McKitterick

Mahesh K Jeerage

Milos Vesely

Petr Cisar

Gang Qian

Christopher Jon Visker

Joel Hesch

Yinzhe Yu

Kirk Schloegel

Vassillos Morellas

Johnson Andrew

Shrikant Rao

In one embodiment, a method for optimizing a set of matched features is provided. The method includes matching features between an optical image and a geo-referenced orthoimage to produce an initial set of matched features. An initial position solution is then determined for the optical image using the initial set of N matched features. The initial set of N matched features are then optimized based on a set of N sub-solutions and the initial position solution, wherein each of the N sub-solutions is a position solution using a different set of (N&#x2212;1) matched features. A refined position solution is then calculated for the optical image using the optimized set of matched features.

Feature set optimization in vision-based positioning

A system is provided for correlating evidence grids. In certain embodiments, the system includes a sensor that generates signals describing a current section of an environment; a memory configured to store measurements of historical sections of the environment; and a processor coupled to the sensor and configured to calculate navigation parameters based on signals received from the sensor. Further, the processor converts the signals received from the sensor into a current evidence grid and removes data from the current evidence grid to form a reduced evidence grid; converts the measurements of historical sections into a historical evidence grid; and correlates the reduced evidence grid with the historical evidence grid by adjusting position and orientation of the reduced evidence grid and the historical evidence grid in relation to one another and calculating correlative values, and searching for a highest correlative value.

Systems and methods for correlating reduced evidence grids

Systems and methods for 3D data based navigation using descriptor vectors are provided. In at least one embodiment, a method for identifying corresponding segments in different frames of data comprises identifying a first segment set in a first frame in multiple frames acquired by at least one sensor, and identifying a second segment set in a second frame in the multiple frames. The method also comprises calculating a first and second set of descriptor vectors, wherein the first and second sets of descriptor vectors comprise a descriptor vector for each segment in the respective first and second segment set, wherein a descriptor vector describes an indexed plurality of characteristics; and identifying corresponding segments by comparing the first set of descriptor vectors against the second set of descriptor vectors, wherein the corresponding segments describe characteristics of the same feature in the environment.

Systems and methods for 3D data based navigation using descriptor vectors

Systems and methods for 3D data based navigation using a watershed method are provided. In at least one embodiment, a method for segmenting three-dimensional frames of data comprises acquiring at least one frame from at least one sensor, wherein the at least one frame provides a three-dimensional description of an environment containing the at least one sensor; and identifying a surface in the at least one frame. The method further comprises computing at least one residual map for the at least one frame based on the orthogonal distance from data points on the surface to at least one polynomial surface fitted to the surface; and segmenting the at least one residual map by performing a watershed algorithm on the residual map.

Systems and methods for 3D data based navigation using a watershed method

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

Systems and methods are provided for selecting landmarks for navigation. In one embodiment, a system comprises an IMU that provides inertial measurements for a vehicle and at least one image sensor that acquires measurements of the vehicle's environment. The system also comprises a processing unit that calculates a navigation solution for the vehicle based on the inertial measurements, identifies a plurality of landmarks in the acquired measurements, and identifies a plurality of usable landmarks from the plurality of landmarks. The processing unit also selects a subset of useable landmarks from the plurality of useable landmarks such that the subset of landmarks has a smaller dilution of precision (DOP) than other possible subsets of landmarks from the plurality of useable landmarks, and calculates an updated navigation solution from the subset of landmarks. The DOP is an amplification factor of measurement errors derived from the geometry of the subset of useable landmarks.

Systems and methods for landmark selection for navigation

A method for autonomous landing of an unmanned aerial vehicle (UAV) comprising: obtaining sensor data corresponding to one or more objects outside of the aircraft using at least one onboard sensor; using the sensor data to create a three dimensional evidence grid, wherein a three dimensional evidence grid is a three dimensional world model based on the sensor data; combining a priori data with the three dimensional evidence grid; locating a landing zone based on the combined three dimensional evidence grid and a priori data; validating an open spots in the landing zone, wherein validating includes performing surface condition assessment of a surface of the open spots; generating landing zone motion characterization, wherein landing zone motion characterization includes characterizing real time landing zone pitching, heaving, rolling or forward motion; processing the three dimensional evidence grid data to generate flight controls to land the aircraft in one of the open spots; and controlling the aircraft according to the flight controls to land the aircraft.

Systems and methods for autonomous landing using a three dimensional evidence grid

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

Systems and methods for navigation using cross correlation on evidence grids are provided. In one embodiment, a system for using cross-correlated evidence grids to acquire navigation information comprises: a navigation processor coupled to an inertial measurement unit, the navigation processor configured to generate a navigation solution; a sensor configured to scan an environment; an evidence grid creator coupled to the sensor and the navigation processor, wherein the evidence grid creator is configured to generate a current evidence grid based on data received from the sensor and the navigation solution; a correlator configured to correlate the current evidence grid against a historical evidence grid stored in a memory to produce displacement information; and where the navigation processor receives correction data derived from correlation of evidence grids and adjusts the navigation solution based on the correction data.

Systems and methods for navigation using cross correlation on evidence grids

Method and system for building a support vector machine binary tree for fast object search. An appearance model can be generated for objects in a database and computed on regions detected in an image frame. A covariance matrix can be utilized for representing the appearance model of the detected regions. The covariance matrix appearance model can be preprocessed and/or transferred into a vector-based format. The data in the vector-based format can be added with a class label to form labeled data. A support vector machine (SVM) can be utilized on the labeled data to generate a classifier with an optimal hyperplane and a margin area in order to hierarchically build a balanced SVM binary tree. A query appearance model can be searched rapidly utilizing the SVM binary tree during a search phase.

Method and system for building a support vector machine binary tree for fast object search

A method includes generating current coarse edge count representation based on current fine grid representation of current section, correlating current edge quantity values of current coarse pixels with historical edge quantity values of historical coarse pixels of historical coarse edge count representation of environment, and identifying first subsection of historical coarse edge count representation with highest correlation to current coarse edge count representation. Each current coarse pixel in current coarse edge count representation represents current fine pixels from current fine grid representation. Fine grid representation of current section of environment is based on data from range and attitude sensor. Each current coarse pixel within current coarse edge count representation includes current edge quantity value that represents quantity of current fine pixels represented by current coarse pixel that include edge. Each historical coarse pixel corresponds to historical fine pixels in historical fine grid representation of environment.

Rapid lidar image correlation for ground navigation

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 navigation device is provided herein comprising an inertial measurement unit (IMU), a camera, and a processor. The IMU provides an inertial measurement to the processor and the camera provides at least one image frame to the processor. The processor is configured to determine navigation data based on the inertial measurement and the at least one image frame, wherein at least one feature is extracted from the at least one image frame based on the navigation data.

Camera and inertial measurement unit integration with navigation data feedback for feature tracking

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

An event detection system includes a processor, an electronic tracking device, and one or more transmitters. Each of the one or more transmitters can be configured to be associated with a particular individual of a group of individuals. The processor can be configured to cluster data from the one or more transmitters, and the processor can be configured to analyze the clustered data to determine a group behavior pattern among the group of individuals. In an embodiment, video data can be combined with the electronic tracking device data in the event detection system.

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Roseville, MN, United States of America

Yunqian Ma