Gopro, Inc.

Hexagon Geosystems Services Ag

Leica Geosystems Ag

Leica Geosystems Technology A/s

Pascal Gohl

Sammy Omari

Joseph Anthony Enke

Garance Bruneau

Axel Murguet

Thomas Gubler

Kielan C Crow

Himay Rashmikant Shukla

Stepan N Moskovchenko

Benjamin P Tankersley

Andreas Jäger

Fabio Diem

Tim Oberhauser

Lukas Schmid

Pascal Strupler

Simon Doessegger

Matthias Wieser

Roman Steffen

Burkhard Böckem

Lukas Heinzle

Tuomo Haarakangas

Tommi Juhani Kauppinen

Ilkka Heikura

Andreas Gerster

Magnus Thibblin

Marko Panjek

Dimitrios Gryparis

The invention relates to a computer implemented method for identifying transparent and/or mirroring plane candidates from measurement data resulting from scanning an environment by a laser scanner emitting distance measuring light pulses.

Computer implemented method for identifying transparent and/or mirroring plane candidates and UAV using the same

A system for 3D surveying of an environment by an unmanned ground vehicle (UGV) and an unmanned aerial vehicle (UAV) has two lidar devices. A reference unit has a first and a second marker in a spatially fixed arrangement. An automatic detection of the first marker is carried out for a coordinative measurement by the first lidar device to determine relative position data for providing relative position information of the first marker with respect to the first lidar device. The relative position data and spatial 3D information is used for an automatic detection and a coordinative measurement of the second marker by the second lidar device. The coordinative measurements are used for a referencing of lidar data of the UGV lidar device and lidar data of the UAV lidar device with respect to a common coordinate system.

System for 3D surveying by a UGV and a UAV with automatic provision of referencing of UGV lidar data and UAV lidar data

A GNSS antenna system for receiving GNSS signals in the L1 and L2/L5 frequency band, and to an unmanned aerial vehicle (UAV) comprising the GNSS antenna system.

GNSS antenna system for receiving multi-band GNSS signals

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

In one aspect of the present disclosure, a module is disclosed for use with a digital image capturing device (DICD) including an integrated sensor-lens assembly (ISLA). The module includes a cradle configured for connection to a housing of the DICD, and at least one dampener that is configured for positioning between the module and the housing of the DICD to reduce vibrations transmitted to the ISLA.

Module isolation

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 construction machine comprising a chassis, a steering, and a powertrain for driving the construction machine by the chassis, an earth-moving tool for working a terrain, and a measuring system having a first measuring unit configured for generating first measuring data in a first detection range and comprising at least a first camera and a first LiDAR scanner configured for rotating a first measuring beam around a first axis and around a second axis non-parallel to the first axis with a rotating speed of at least 0.5 Hz with respect to each axis, an interface connecting the first measuring unit to a computer configured for, based on the first measuring data, at least one of generating a three-dimensional model of the terrain within the first detection range, identifying an obstacle or a person within the first detection range, and controlling the steering, the powertrain, and/or the earth-moving tool.

Construction machine with measuring system and construction site measuring system

The disclosure describes systems and methods for a stabilization mechanism. The stabilization mechanism may be used in conjunction with an imaging device. The method may be performed by a control system of the stabilization mechanism and includes obtaining a device setting from an imaging device. The method may also include obtaining a configuration of the stabilization mechanism. The method includes determining a soft stop based on the device setting, the configuration, or both. The soft stop may be a virtual hard stop that indicates to the stabilization mechanism to reduce speed as a field of view of the imaging device approaches the soft stop. The method may also include setting an image stabilization mechanism parameter based on the determined soft stop.

Method and system for user feedback in a motion constrained image stabilization 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.

Growing community of inventors

Winterthur, Switzerland

Pascal Gohl