The Boeing Company

Urc Ventures, Inc.

Massachusetts Institute of Technology

Charles A Erignac

Paul Murray

James J Troy

Jan-Michael Frahm

David Bradley Boardman

Srinivas Kapaganty

Jared Scott Heinly

Brian Sanderson Clipp

Ben Semerjian

John Lyle Vian

Emad William Saad

Dragos Dorin Margineantu

Ming Wan

Ali Reza Mansouri

Gregory John Clark

Stefan Richard Bieniawski

Hui Li

Michael Drumheller

David J Finton

Jonathan Patrick How

Brett M Bethke

Paul E R Pigg

Ronald Carl Provine

Khaled Abdel-Motagaly

Richard H Wojcik

Mario J Valenti

Anthony James Jacobson

Paul E Pigg

Jonathan B How

Ali R Mansouri

Emad W Saad

Ronald C Provine

John L Vian

Stefan R Bieniawski

Gregory J Clark

Systems and methods for autonomous taxi route planning for an aircraft. Clearance communication is received from a ground control station. A planning problem is generated from the clearance communication and sent to a route planner. The route planner receives the planning problem and plans an executable taxi route. Planning the executable taxi route can include generating a complete breadth first search graph from a start pose to a destination, pruning the graph, minimizing the graph, refining the graph, and extracting the shortest path from the graph.

System and method for clearance-based taxi route planning

Systems and methods for autonomous taxi route execution for an aircraft. Clearance communication is received from a ground control station. A plurality of objects is generated from the clearance communication. Next, a context data structure representing a planned taxi route is generated. Last, the planned taxi route is executed.

System and method for clearance-based taxi route execution

Techniques are described for analyzing images acquired via mobile devices in various ways, including to estimate measurements for one or more attributes of one or more objects in the images and/or to perform automated verification of such attribute measurements. For example, the described techniques may be used to measure the volume of a stockpile of material or other large object, based on images acquired via a mobile device that is moved around some or all of the object. The calculation of object volume and/or other determined object information may include generating and manipulating one or more computer models of the object from selected images. In addition, further automated verification activities may be performed for such computer model(s) and resulting object attribute measurements, such as based on analyzing one or more types of information that reflect accuracy and/or completeness of the computer model(s).

Verifying object measurements determined from mobile device images

Techniques are described for analyzing images acquired via mobile devices in various ways, including to estimate measurements for one or more attributes of one or more objects in the images, as well as determine changes over time in objects and their measurements based on images acquired at different times. For example, the described techniques may be used to measure the volume of a stockpile of material or other large object, based on images acquired via a mobile device that moves around some or all of the object. The calculation of object volume and/or other determined object information may include generating and manipulating one or more computer models of the object from selected images, and determining changes may include comparing different models for different times. In addition, further automated activities may include displaying, presenting or otherwise providing information about some or all of the determined information.

Determining changes in object structure over time using mobile device images

Methods and systems described herein enable self-supervised road detection in images. The method includes receiving an image, segmenting the image into at least one fragment based at least in part on at least one pixel feature, determining, using a processor, a road likeness score for the at least one fragment based at least in part on a medial radius, and identifying roads based at least in part on the road likeness score.

Systems and methods for identifying roads in images

Techniques are described for analyzing images acquired via mobile devices in various ways, including to estimate measurements for one or more attributes of one or more objects in the images. For example, the described techniques may be used to measure the volume of a stockpile of material or other large object, based on images acquired via a mobile device that is carried by a human user as he or she passes around some or all of the object. During the acquisition of a series of digital images of an object of interest, various types of user feedback may be provided to a human user operator of the mobile device, and particular images may be selected for further analysis in various manners. Furthermore, the calculation of object volume and/or other determined object information may include generating and manipulating a computer model or other representation of the object from selected images.

Determining object volume from mobile device images

A system includes a transformation module, alignment module and aligned localization module. The transformation module is configured to receive first and second pose estimates of a mobile platform movable within an environment. The first and second pose estimates are relative to different, respective first and second digital maps of the environment in different, respective first and second coordinate systems. The transformation module, then, may be configured to calculate a geometric transform between the first and second digital maps based on the first and second pose estimates. The alignment module may be configured to align the first and second digital maps based on the geometric transform, and thereby generate an aligned digital map. And the aligned localization module may be configured to localize the mobile platform relative to the aligned digital map, and thereby calculate an aligned pose estimate of the mobile platform.

Augmented mobile platform localization

Computer-implemented methods, systems, and computer-readable storage media are disclosed to coordinate a plurality of devices in performing a task. A particular computer-implemented method includes storing updated status information at a device where the updated status information reflects a change in a vote for a task state of one or more of a plurality of devices. A first updated status message is sent to one or more of the plurality of devices where the first updated status message communicates the updated status information. A task consensus at the device is updated when the updated status information indicates that at least a predetermined quantity of the plurality of devices agrees on the task status.

Consensus based distributed task execution

A system configured to enable three-dimensional localization and mapping is provided. The system includes a mobile device and a computing device. The mobile device includes an inertial measurement unit and a three-dimensional image capture device. The computing device includes a processor programmed to receive a first set of inertial measurement information from the inertial measurement unit, determine a first current position and orientation of the mobile device based on a defined position and orientation of the mobile device and the first set of inertial measurement information, receive three-dimensional image data of the environment from the three-dimensional image capture device, determine a second current position and orientation of the mobile device based on the received three-dimensional image data and the first current position and orientation of the mobile device, and generate a three-dimensional representation of an environment with respect to the second current position and orientation of the mobile device.

Methods and apparatus for three-dimensional localization and mapping

A k-covered motion capture volume is modeled. The modeling includes representing the k-covered motion capture volume with a distance field.

Modeling motion capture volumes with distance fields

A method and apparatus for identifying visibility of targets. A first function is selected to indicate whether locations in an environment are at a surface for objects in the environment, outside the surface, or inside the surface. First volumes are formed for the environment. Each volume in the first volumes has a size selected such that a difference between first interpolated values for each volume and first values generated using the first function for each volume is within a threshold. Second volumes are formed for the environment. Each of the second volumes has a size selected such that a difference between second interpolated values for each volume and second values generated using a second function for each volume is within a threshold. The second values are minimum values along lines from each volume in the second number of volumes to a target in the environment.

Model of visibility of targets

A method is disclosed for controlling at least one remotely operated unmanned object. The method may involve defining a plurality of body movements of an operator that correspond to a plurality of operating commands for the unmanned object. Body movements of the operator may be sensed to generate the operating commands. Wireless signals may be transmitted to the unmanned object that correspond to the operating commands that control operation of the unmanned object.

System and method for controlling swarm of remote unmanned vehicles through human gestures

Systems and methods are disclosed for haptics-enabled teleoperation of vehicles and other devices, including remotely-controlled air, water, and land-based vehicles, manufacturing robots, and other suitable teleoperable devices. In one embodiment, a system for teleoperation of a vehicle comprises a control component configured to provide position and orientation control with haptic force feedback of the vehicle based on a position measurement of the vehicle and configured to function in a closed-loop feedback manner. In a particular embodiment, the position measurement may include six degree-of-freedom position data provided by a motion capture system to the control and/or haptic I/O components of the application. The system may also use differences in position and/or velocity between the vehicle and a haptic I/O device for feedback control.

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Seattle, WA, United States of America

Charles A Erignac