Abstract: Methods, apparatus, systems, and articles of manufacture for facilitating task execution using a drone are disclosed. An example method includes accessing a result of a task performed by a task executor. The result includes one or more images of a task objective captured by the task executor. The result is validated based on a task definition provided by a task issuer. In response to the validation of the result indicating that the result complies with the task definition, the result is provided to the task issuer, and a reward is issued to the task executor.

Abstract: Methods, systems and apparatuses may include technology that compresses a central region of an image to a central level of detail and compresses one or more peripheral regions of the image to one or more peripheral levels of detail that are less than the central level of detail, wherein the central region and the peripheral region(s) are fixed. Additionally, the technology may send the compressed central region and the compressed peripheral region(s) to a remote display. In one example, the central region and the peripheral region(s) are independent of eye movement with respect to the remote display.

Abstract: Herein is disclosed a bounding-volume based unmanned aerial vehicle illumination management system comprising one or more processors, configured to define a plurality of bounding volumes within a region of unmanned aerial vehicle flight; determine a subset of unmanned aerial vehicles within a bounding volume according to an unmanned aerial vehicle flight plan; determine a combined lighting value of the subset of unmanned aerial vehicles according to an unmanned aerial vehicle illumination plan; and determine a surface illumination corresponding to the combined lighting value of one or more bounding volumes.

Abstract: A sensor calibrator comprising one or more processors configured to receive sensor data representing a calibration pattern detected by a sensor during a period of relative motion between the sensor and the calibration pattern in which the sensor or the calibration pattern move along a linear path of travel; determine a calibration adjustment from the plurality of images; and send a calibration instruction for calibration of the sensor according to the determined calibration adjustment. Alternatively, a sensor calibration detection device, comprising one or more processors, configured to receive first sensor data detected during movement of a first sensor along a route of travel; determine a difference between the first sensor data and stored second sensor data; and if the difference is outside of a predetermined range, switch from a first operational mode to a second operational mode.

Abstract: Techniques are disclosed to process event data acquired via components that may be integrated as part of the autonomous vehicle's operational system, such as cameras and GPS systems. The event data may then be processed to generate processed event data based upon an analysis of various conditions occurring during, prior to, or shortly after a user's trip in an autonomous vehicle. The processed event data may represent one or more portions of sharable digital content such as a pre-edited video clip, a montage, an image, a series of images, etc. The user may access and then share these portions of sharable content to various platforms, such as social media platforms.

Abstract: Apparatus and method for correcting image regions following upsampling or frame interpolation. For example, one embodiment of an apparatus comprises a machine-learning engine to evaluate at least a first image in a sequence of images generated by a real-time interactive application, the machine learning engine to responsively use previously learned data to generate an upsampled or interpolated image comprising a plurality of pixel patches. In one embodiment, each pixel patch is associated with a confidence value reflecting how accurately the pixel patch was generated by the machine learning engine. A selective ray tracing engine identifies a first pixel patch to be corrected based a first confidence value corresponding to the first pixel patch being lower than a threshold and performs ray tracing operations on a first portion of the first image to generate a corrected first pixel patch.

Abstract: Methods and apparatus to reduce a depth map size for use in a collision avoidance system are described herein. Examples described herein may be implemented in an unmanned aerial vehicle. An example unmanned aerial vehicle includes a depth sensor to generate a first depth map. The first depth map includes a plurality of pixels having respective distance values. The unmanned aerial vehicle also includes a depth map modifier to divide the plurality of pixels into blocks of pixels and generate a second depth map having fewer pixels than the first depth map based on distance values of the pixels in the blocks of pixels. The unmanned aerial vehicle further includes a collision avoidance system to analyze the second depth map.

Abstract: According to various aspects, a modified-reality device may be described, the modified-reality device including: a head-mounted device including one or more displays, wherein the one or more displays are configured to receive image data representing at least an image element and to display a modified-reality image including at least the image element; one or more sensors configured to provide head tracking data associated with a location and an orientation of the head-mounted device; and a processing arrangement configured to receive flight data associated with a flight of an unmanned aerial vehicle, generate the image data representing at least the image element based on the head tracking data and the flight data, and provide the image data to the one or more displays.

Abstract: According to various aspects, a controller includes one or more processors configured to: determine when a predicted event occurs, at which a velocity of a vehicle is changed; and provide an instruction for a reorientation of one or more seats of the vehicle based on when the predicted event occurs.

Abstract: One embodiment of a virtual reality apparatus comprises: a graphics processing engine comprising a plurality of graphics processing stages, the graphics processing engine to render a plurality of image frames for left and right displays of a head mounted display (HMD); and foveation control hardware logic to independently control two or more of the plurality of graphics processing stages based on feedback received from an eye tracking module of the HMD, the feedback indicating a foveated region selected based on a current or anticipated direction of a user's gaze, the foveation control hardware logic to cause the two or more of the graphics processing stages to process the foveated region differently than other regions of the image frames.

Abstract: A vehicle relocator may include one or more processors, which are configured to receive first sensor data representing a current or predicted weather condition; receive second sensor data representing at least position or a vicinity of a vehicle; determine from at least the first sensor data and the second sensor data a risk of damage to the vehicle; and if the determined risk is outside of a target range, send an instruction for the vehicle to travel to an alternate location.

Abstract: Herein is disclosed an unmanned aerial vehicle navigation system including one or more cameras configured to detect user image data; and one or more processors configured to determine from the user image data a user eye orientation; select according to a selection criterion a navigation behavior based on the user eye orientation; and determine a navigation instruction to cause the unmanned aerial vehicle to travel in accordance with the navigation behavior.

Abstract: Apparatus, method and storage medium associated with a beverage dispensing apparatus are disclosed herein. In embodiments, a beverage dispensing apparatus may include a dispenser to dispense a beverage into a beverage container placed underneath the dispenser; one or more sensors to sense and collect depth data associated with the beverage container; and a controller coupled to the dispenser and the one or more sensors to control the dispenser's dispensation of the beverage, in accordance with a capacity of the beverage container inferred based at least in part on the collected depth data associated with the beverage container. Other embodiments may be disclosed or claimed.

Abstract: A luminous intensity compensator, comprising a light source; a sensor, configured to receive sensor information representing a position of the light source, and to output sensor data representing the position of the light source; one or more processors, configured to determine from the sensor data a distance between the light source and a reference point; determine a loss factor of a wavelength based on the determined distance between the light source and the reference point; determine a compensated luminous intensity to yield a target luminous intensity of the wavelength after luminous intensity reduction due to the loss factor; and output control data to control the light source to emit the wavelength at the compensated luminous intensity.

Abstract: A system for unmanned aerial vehicle alignment including: an image sensor, configured to obtain an image of unmanned aerial vehicles and provide to a processor image data corresponding to the obtained image, the processor, configured to determine from the image data image positions of the unmanned aerial vehicles, determine an average position of the unmanned aerial vehicles relative to a first axis based on the image positions, determine an average line that extends along a second axis through the average position, wherein the first and second axes are perpendicular to each other, determine a target position of one of the unmanned aerial vehicles based on a relationship between its respective image position and a target alignment, and determine an adjustment instruction to direct said one of the unmanned aerial vehicles toward the target position, and provide the adjustment instruction to said one of the unmanned aerial vehicles.

Abstract: A risk detection and avoidance device comprises one or more image sensors, configured to provide image sensor input data representing a sensor image of a vicinity of the device; and one or more processors, configured to detect from the image sensor data an overpass and one or more persons on the overpass; determine from the image sensor data a falling-object probability associated with the detected one or more persons on the overpass, according to a first logic; determine from the image sensor data one or more falling-object evasion factors associated with a vicinity of the device; and if the falling-object probability exceeds a predetermined threshold, determine a harm avoidance maneuver based at least on the one or more falling-object evasion factors, according to a second logic, and send an instruction comprising the harm avoidance maneuver.

Abstract: Systems and methods may provide a plurality of distortion meshes that compensate for radial and chromatic aberrations created by optical lenses. The plurality of distortion meshes may include different lens specific parameters that allow the distortion meshes to compensate for chromatic aberrations created within received images. The plurality of distortion meshes may correspond to a red color channel, green color channel, or blue color channel to compensate for the chromatic aberrations. The distortion meshes may also include shaped distortions and grids to compensate for radial distortions, such as pin cushion distortions. In one example, the system uses a barrel-shaped distortion and a triangulation grid to compensate for the distortions created when the received image is displayed on a lens.

Abstract: An apparatus, method and computer readable medium for a voice-controlled camera with artificial intelligence (AI) for precise focusing. The method includes receiving, by the camera, natural language instructions from a user for focusing the camera to achieve a desired photograph. The natural language instructions are processed using natural language processing techniques to enable the camera to understand the instructions. A preview image of a user desired scene is captured by the camera. Artificial Intelligence (AI) is applied to the preview image to obtain context and to detect objects within the preview image. A depth map of the preview image is generated to obtain distances from the detected objects in the preview image to the camera. It is determined whether the detected objects in the image match the natural language instructions from the user.

Abstract: Herein is disclosed an unmanned aerial vehicle alignment system comprising one or more image sensors, configured to obtain an image of a plurality of unmanned aerial vehicles and provide to one or more processors image data corresponding to the obtained image; one or more processors, configured to detect from the image data image positions of the plurality of unmanned aerial vehicles; derive a target position based on a relationship between an image position and a target alignment; and determine an adjustment instruction to direct an unmanned aerial vehicle toward the target position.

Abstract: Herein is disclosed a detection device comprising one or more sensors, configured to receive sensor input from a vicinity of a first vehicle, and to generate sensor data representing the received sensor input; one or more processors, configured to detect a second vehicle from the received sensor data; determine from the sensor data a region of a first type relative to the second vehicle and a region of a second type relative to the second vehicle; and control the first vehicle to avoid or reduce travel in the one or more regions of the first type or to travel from a region of the first type to a region of the second type.