Gopro, Inc.

Sammy Omari

Pascal Gohl

Joseph Anthony Enke

Fabio Diem

Tim Oberhauser

Lukas Schmid

Stepan N Moskovchenko

Benjamin P Tankersley

Garance Bruneau

Axel Murguet

Andreas Jäger

Simon Doessegger

Thomas Gubler

Jean-Bernard Berteaux

This disclosure relates to systems and methods for controlling an unmanned aerial vehicle. Boundaries of a user-defined space may be obtained. The boundaries of the user-defined space may be fixed with respect to some reference frame. A user-defined operation associated with the user-selected space may be obtained. Position of the unmanned aerial vehicle may be tracked during an unmanned aerial flight. Responsive to the unmanned aerial vehicle entering the user-defined space, the unmanned aerial vehicle may be automatically controlled to perform the user-defined operation.

Systems and methods for controlling an unmanned aerial vehicle

A vehicle includes systems and methods to guide the vehicle. A sensor detects objects around the vehicle and processor includes a predicted motion component and a predicted imaging component to avoid objects around the vehicle. The system and method then avoid the object if the processor determines that an intersection will occur between the object and the vehicle.

Systems and methods for vehicle guidance

Controlling an unmanned aerial vehicle may include obtaining a first image from a fixed orientation image capture device of the unmanned aerial vehicle, obtaining a second image from an adjustable orientation image capture device of the unmanned aerial vehicle, obtaining feature correlation data based on the first image and the second image, obtaining relative image capture device orientation calibration data based on the feature correlation data, the relative image capture device orientation calibration data indicating an orientation of the adjustable orientation image capture device relative to the fixed orientation image capture device, obtaining relative object orientation data based on the relative image capture device orientation calibration data, the relative object orientation data representing a three-dimensional orientation of an external object relative to the adjustable orientation image capture device, and controlling a trajectory of the unmanned aerial vehicle in response to the relative object orientation data.

Relative image capture device orientation calibration

Systems and methods are disclosed for image signal processing. For example, methods may include, based on a sequence of orientation estimates for an image sensor and an orientation setpoint, invoking a mechanical stabilization system to adjust an orientation of the image sensor toward the orientation setpoint; receiving an image from the image sensor; determining an orientation error between the orientation of the image sensor and the orientation setpoint during capture of the image; and, based on the orientation error, invoking an electronic image stabilization module to correct the image for a rotation corresponding to the orientation error to obtain a stabilized image.

Combined mechanical and electronic image stabilization

Controlling an unmanned aerial vehicle to traverse a portion of an operational environment of the unmanned aerial vehicle may include obtaining an object detection type, obtaining object detection input data, obtaining relative object orientation data based on the object detection type and the object detection input data, and performing an object avoidance operation based on the relative object orientation data. The object detection type may be monocular object detection, which may include obtaining the relative object orientation data by obtaining motion data indicating a change of spatial location for the unmanned aerial vehicle between obtaining the first image and obtaining the second image based on searching along epipolar lines to obtain optical flow data.

Adaptive object detection

A method is provided for controlling a movable imaging assembly having a movable platform and an imaging device coupled to and movable relative to the movable platform. The method includes receiving user inputs that define an MIA position relative to a target and a frame position of the target within image frames captured by the imaging device. The user inputs include a horizontal distance, a circumferential position, and a horizontal distance that define the MIA position, and include a horizontal frame position and a vertical frame position that define the frame position. The method further includes predicting a future position of the target for a future time, and moving the MIA to be in the MIA position at the future time and moving the imaging device for the target to be in the frame position for an image frame captured at the future time.

Subject tracking systems for a movable imaging system

Systems and methods are disclosed for image signal processing. For example, methods may include determining an orientation setpoint for an image sensor; based on a sequence of orientation estimates for the image sensor and the orientation setpoint, invoking a mechanical stabilization system to adjust an orientation of the image sensor toward the orientation setpoint; receiving an image from the image sensor; determining an orientation error between the orientation of the image sensor and the orientation setpoint during capture of the image; based on the orientation error, invoking an electronic image stabilization module to correct the image for a rotation corresponding to the orientation error to obtain a stabilized image; and storing, displaying, or transmitting an output image based on the stabilized image.

Controlling an unmanned aerial vehicle to traverse a portion of an operational environment of the unmanned aerial vehicle may include obtaining an object detection type, obtaining object detection input data, obtaining relative object orientation data based on the object detection type and the object detection input data, and performing a collision avoidance operation based on the relative object orientation data. The object detection type may be monocular object detection, which may include obtaining the relative object orientation data by obtaining motion data indicating a change of spatial location for the unmanned aerial vehicle between obtaining the first image and obtaining the second image based on searching along epipolar lines to obtain optical flow data.

This disclosure relates to systems and methods for vehicle guidance. Stereo images may be obtained at different times using a stereo image sensor. A depth image may be determined based on an earlier obtained pair of stereo images. The depth image may be refined based on predictions of an earlier stereo image and a later obtained stereo image. Depth information for an environment around a vehicle may be obtained. The depth information may characterize distances between the vehicle and the environment around the vehicle. A spherical depth map may be generated from the depth information. Maneuver controls for the vehicle may be provided based on the spherical depth map.

Systems and methods for vehicle guidance using depth map generation

This disclosure relates to systems and methods for determining predicted risk for a flight path of an unmanned aerial vehicle. A previously stored three-dimensional representation of a user-selected location may be obtained. The three-dimensional representation may be derived from depth maps of the user-selected location generated during previous unmanned aerial vehicle flights. The three-dimensional representation may reflect a presence of objects and object existence accuracies for the individual objects. The object existence accuracies for the individual objects may provide information about accuracy of existence of the individual objects within the user-selected location. A user-created flight path may be obtained for a future unmanned aerial flight within the three-dimensional representation of the user-selected location. Predicted risk may be determined for individual portions of the user-created flight path based upon the three-dimensional representation of the user-selected location.

Systems and methods for determining predicted risk for a flight path of an unmanned aerial vehicle

Disclosed is a system and method for calibrating a magnetometer. The method comprises responsive to a determination that a magnetic inclination is less than a threshold, measuring first magnetic field data by detecting a magnetic field with the magnetometer through a first rotation path, measuring second magnetic field data by detecting the magnetic field with the magnetometer through a second rotation path, and determining calibration values for the magnetometer based on the measured first magnetic field data and the measured second magnetic field data.

Adaptive compass calibration based on local field conditions

A method for tracking a subject in successive image frames includes obtaining previous image frames with an imaging device, processing the previous image frames, obtaining motion information of the imaging device and a subject, determining a region of interest, obtaining a subsequent image frame, and processing the region of interest. The processing includes determining previous frame positions of the subject therein. The motion information is obtained with sensors physically associated with one or more of the imaging device and the subject. The region of interest is located in a predetermined spatial relationship relative to a predicted frame position of the subject.

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Zurich, Switzerland

Sammy Omari