Disney Enterprises, Inc.

Eth Zurich

Eth Zurich (eidgenoessische Technische Hochschule Zurich)

Disney Research Zurich

Paul A Beardsley

Thabo D Beeler

Bernd Bickel

Javier Alonso Mora

Robert Walker Sumner

Martin M Rufli

Andreas Breitenmoser

Roland Siegwart

Markus H Gross

Markus Gross

Iain Matthews

Katsu Yamane

Javier Alonso-Mora

Anurag Sai Vempati

Alexander Sorkine Hornung

Scott Frazier Watson

Derek Edward Bradley

Federico Perazzi

Christopher James Purvis

Daniel James Reetz

Igor Gilitschenski

Marc Pollefeys

Fabian Hahn

Steve Marschner

Aparna Taneja

Bob Sumner

Pirmin Mattmann

Joern Rehder

Jereon Van Baar

Juan Nieto

Mina Samir Fekry Kamel

Michael Eriksson

Nikola Stilinović

Michael Arigoni

Robert Simpson

Silvan Fuhrer

Dario Mammolo

Michael Burri

Michael Bischoff

Thomas Stastny

Lennon Rodgers

David Krummenacher

Anselm Grundhoefer

Stefan Leutenegger

Matthias Burri

Konrad Rudin

Roland Yves Siegwart

Schaffner Andreas

Anton Ledergerber

Miro KÄCH

Johannes Weichart

Luca MÜRI

Simon Laube

Lukas Mosimann

Matthias Krebs

Randy Michaud

Lukas Gasser

Nicolas Vuilliomenet

Daniel Meier

Claudio Ruch

A painting system that makes use of drones such as modified quadrotors. The drone includes a support arm that carries a paint nozzle configured for pan and tilt motion. A power supply line is connected from an external power supply to the drone to allow extended flight time. A paint supply line is also connected from an external paint supply to the drone to allow extended painting time and/or surface coverage with each flight. The drone has an onboard controller so painting is autonomous with no human input being required. The drone stores a 3D model of the target structure annotated with the drone trajectory plus commands to control the pan-tilt paint nozzle to perform the painting. At runtime, the controller uses a sensor to view the target structure and localizes itself. The drone then traverses the stored trajectory and implements the painting commands to paint the 3D structure's surfaces.

Drone-based painting system

According to one implementation, a robotic system for performing large-scale environmental mapping in real-time includes a mobile reconnaissance unit having a color sensor, a depth sensor, and a graphics processing unit (GPU) with a GPU memory, and a navigation unit communicatively coupled to the mobile reconnaissance unit and having a central processing unit (CPU) with a CPU memory. The robotic system begins a three-dimensional (3D) scan of an environment of the mobile reconnaissance unit using the color sensor and the depth sensor, and generates mapping data for populating a volumetric representation of the environment. The robotic system continues the 3D scan of the environment using the color sensor and the depth sensor, updates the mapping data, and partitions the volumetric representation between the GPU memory and the CPU memory based on a memory allocation criteria.

Large-scale environmental mapping in real-time by a robotic system

An omnidirectional camera apparatus configured to facilitate omnidirectional stereo imaging is described. The apparatus may include a first convex mirror, a first camera disposed at the first convex mirror, a second convex mirror, and a second camera disposed at the second convex mirror. The first convex mirror and the second convex mirror may be arranged such that a first mirrored surface of the first convex mirror and a second mirrored surface of the second convex mirror may face each other. The first camera may capture imagery reflected off the second convex mirror. The second camera may capture imagery reflected off the first convex mirror. A method of calibrating an omnidirectional camera apparatus is also described.

Systems, methods, and apparatuses for stereoscopic imaging

A vehicles configured for navigating surface transitions. Navigation of surface transitions is controlled by information obtained by sensors carried by the vehicle. The vehicle may be propelled forward using force generated by tiltable propellers carried by the vehicle.

Vehicles configured for navigating surface transitions

A three-dimensional coordinate position of a calibration device is determined. Further, a code is emitted to an image capture device. The code indicates the three-dimensional coordinate position of the calibration device. In addition, an image of light emitted from the calibration device is captured. The light includes the code. An image capture device three-dimensional coordinate position of the calibration device is calibrated according to the real world three-dimensional coordinate position of the calibration device indicated by the code.

Image capture device calibration

A method of docking and recharging using a base station and a station-mating frame on the multicopter. The base station includes an upward-facing camera that is used by a docking controller to detect the presence, position, and orientation of a frame, with infrared light-emitting diodes arranged in a predefined pattern. The controller of the base station acts to emit wireless signals to the multicopter to guide the multicopter with its station-mating frame to a predefined position above the base station. The controller transmits a wireless signal to the multicopter to reduce thrust, and the multicopter lowers itself onto a sloped receiving surface that may be arranged in a crown pattern to provide passive gravity-driven centering, which causes the station-mating frame to slide to a lowest vertical point of the receiving assembly. A locking mechanism engages to lock the frame in place and provide electrical contact for recharging.

Robust and autonomous docking and recharging of quadrotors

To generate a pixel-accurate depth map, data from a range-estimation sensor (e.g., a time-of flight sensor) is combined with data from multiple cameras to produce a high-quality depth measurement for pixels in an image. To do so, a depth measurement system may use a plurality of cameras mounted on a support structure to perform a depth hypothesis technique to generate a first depth-support value. Furthermore, the apparatus may include a range-estimation sensor which generates a second depth-support value. In addition, the system may project a 3D point onto the auxiliary cameras and compare the color of the associated pixel in the auxiliary camera with the color of the pixel in reference camera to generate a third depth-support value. The system may combine these support values for each pixel in an image to determine respective depth values. Using these values, the system may generate a depth map for the image.

Sensor fusion for depth estimation

The present disclosure discloses a system and method for creating a customized guest experience at an amusement park. In one example, the method includes capturing by a foot sensor a first foot shape corresponding to at least one foot in a pair of feet of a guest and capturing by a camera a first foot appearance corresponding to at least one foot in the first pair of feet receiving guest data from the guest. The method also includes generating a first foot model using the first foot shape and the first foot appearance and tagging the first foot model with the guest data. The foot model can be used to identify a particular guest and the guest data can be used to output a customized guest experience to the guest.

System and method using foot recognition to create a customized guest experience

Techniques are provided to model hair and skin. Multiscopic images are received that depict at least part of a subject having hair. The multiscopic images are analyzed to determine hairs depicted. Two-dimensional hair segments are generated that represent the hairs. Three-dimensional hair segments are generated based on the two-dimensional hair segments. A three-dimensional model of skin is generated based on the three-dimensional hair segments.

Coupled reconstruction of hair and skin

A method for providing shared control over movement of a vehicle within a space. The method includes receiving user input related to a velocity and a direction for the vehicle within the space. The method includes processing the user input to selectively adjust the velocity and the direction desired by the user based on a set of predefined constraints to generate a trajectory for the vehicle for an upcoming time period. The method includes operating drive mechanisms in the vehicle based on the trajectory to move the vehicle from a first position to a second position within the space during the upcoming time period. A grid map defining locations of obstacles in the space may be used to define the trajectory to avoid collisions, and a guidance trajectory may be used to further control movement to achieve a desired throughput and control vehicle movement within particular portions of the space.

Shared control of semi-autonomous vehicles including collision avoidance in multi-agent scenarios

Techniques are disclosed for controlling robot pixels to display a visual representation of an input. The input to the system could be an image of a face, and the robot pixels deploy in a physical arrangement to display a visual representation of the face, and would change their physical arrangement over time to represent changing facial expressions. The robot pixels function as a display device for a given allocation of robot pixels. Techniques are also disclosed for distributed collision avoidance among multiple non-holonomic robots to guarantee smooth and collision-free motions. The collision avoidance technique works for multiple robots by decoupling path planning and coordination.

Display with robotic pixels

Techniques are disclosed for controlling robot pixels to display a visual representation of a real-world video texture. Mobile robots with controllable color may generate visual representations of the real-world video texture to create an effect like fire, sunlight on water, leaves fluttering in sunlight, a wheat field swaying in the wind, crowd flow in a busy city, and clouds in the sky. The robot pixels function as a display device for a given allocation of robot pixels. Techniques are also disclosed for distributed collision avoidance among multiple non-holonomic and holonomic robots to guarantee smooth and collision-free motions.

Robotic texture

High-quality passive performance capture using anchor frames derives in part from a robust tracking algorithm. Tracking is performed in image space and uses an integrated result to propagate a single reference mesh to performances represented in the image space. Image-space tracking is computed for each camera in multi-camera setups. Thus, multiple hypotheses can be propagated forward in time. If one flow computation develops inaccuracies, the others can compensate. This yields results superior to mesh-based tracking techniques because image data typically contains much more detail, facilitating more accurate tracking. Moreover, the problem of error propagation due to inaccurate tracking in image space can be dealt with in the same domain in which it occurs. Therefore, there is less complication of distortion due to parameterization, a technique used frequently in mesh processing algorithms.

High-quality passive performance capture using anchor frames

Growing community of inventors

Zurich, Switzerland

Paul A Beardsley