Apple Inc.

Micah P Kalscheur

Jawad Nawasra

Evan C Cull

Richard E Bills

Ryan A Gibbs

Suppawan Kleekajai

Feng Tang

Michael Ross Malone

Matthew C Waldon

Joshua M Susskind

Ashwin Wagadarikar

A light beam scanning device includes a lens element assembly which dynamically adjusts a divergence of the beam. The lens element assembly can include multiple lens elements, one or more of which translates parallel to the light beam to adjust beam divergence. Divergence adjustment can include adjusting the beam divergence along one or more cross sectional axes of the beam. Beam divergence can be adjusted between consecutive scans, during a scan, etc. Beam divergence can be adjusted based on the field of view and scan rate. Beam divergence adjustment can enable dynamic adjustment of the spot size of the beam, which can enable the apparatus to adjust between scanning a wide divergence beam to detect objects in a scene and scanning a narrow divergence beam to generate detailed point clouds of the detected objects. Beam divergence adjustment can enable adjustment of reflection point intensity, enabling detection of low-reflectivity objects.

Actuated optical element for light beam scanning device

A system for detecting a vehicle includes a light source configured to emit a light signal. The system also includes a receiver sensor configured to receive a reflected light signal based at least in part on the light signal reflected from a plurality of reflectors. The system also includes a controller, the controller configured to identify an arrangement pattern of the plurality of reflectors based at least in part on the reflected light signal and determine that plurality of reflectors are coupled to another vehicle based at least in part on an identification of the arrangement pattern.

Enabling lidar detection

A facial recognition authentication process may utilize images of a user's face that are captured while the user is being illuminated using both flood infrared illumination and patterned illumination (e.g., speckle pattern illumination). As the user's face is illuminated by both flood infrared illumination and patterned illumination, the captured images may include both flood infrared illumination data and depth map image data. Flood infrared illumination data may be generated from the images to assess two-dimensional features of the user in the captured images. Depth map image data may be generated from the pattern data in the images to assess three-dimensional (depth) features of the user in the captured images. The flood infrared illumination data and the depth map image data may be used separately by facial recognition authentication process to attempt to authenticate the user in the captured images as an authorized user of the device.

Hybrid mode illumination for facial recognition authentication

Methods and systems for multi-sensor real-time alignment and calibration are disclosed herein. A vehicle includes a projector configured to project a pattern on a target location. The vehicle also includes one or more sensors. The one or more sensors are configured to capture sensor data from the target location and detect the pattern at the target location. The vehicle further includes a calibration system configured to determine whether a particular sensor of the one or more sensors is out of alignment based on a relative position of the pattern as detected by the one or more sensors. The calibration system is also configured to, based on a determination that the particular sensor is out of alignment, adjust an alignment of the particular sensor based on the sensor data and the diffractive pattern.

Multi-sensor real-time alignment and calibration

Subepidermal imaging of a face may be used to assess subepidermal features such as blood vessels (e.g., veins) when the device is attempting to authenticate a user in a facial recognition authentication process. Assessment of the subepidermal features may be used to distinguish between users that have closely related facial features (e.g., siblings or twins) in situations where the facial recognition authentication process has less certainty in a decision about recognition of the user's face as an authorized user.

Vein matching for difficult biometric authentication cases

Aspects of the present disclosure involve example systems and methods for remote sensing of objects. In one example, a system includes a light source, a camera, a light source timing circuit, an exposure window timing circuit, and a range determination circuit. The light source timing circuit is to generate light pulses using the light source. The exposure window timing circuit generates multiple exposure windows for the light pulses for the camera, each of the multiple exposure windows representing a corresponding first range of distance from the camera. The range determination circuit processes an indication of an amount of light captured at the camera during an opening of each of the multiple exposure windows for one of the light pulses to determine a presence of an object within a second range of distance from the camera, the second range having a lower uncertainty than the first range.

Remote sensing for detection and ranging of objects

Aspects of the present disclosure involve example systems and methods for remote sensing of objects. In one example, a system includes a light source, a camera, a light radar (lidar) system, a region of interest identification circuit, and a range refining circuit. The region of interest identification circuit identifies a region of interest corresponding to an object and a first range of distance to the region of interest using the camera and the light source. The range refining circuit probes the region of interest to refine the first range to a second range of distance to the region of interest using the lidar system, the second range having a lower uncertainty than the first range.

A system includes a memory storing computer-readable instructions and a processor to execute the instructions to perform operations including generating multiple exposure windows for light pulses for a camera, the multiple exposure windows having a sequence comprising a first exposure window having an opening for a duration of time and each other exposure window of the multiple exposure windows having an opening for the duration of time except for a closing for a subset of the duration of time corresponding to a distance from one of a light source and the camera, wherein none of the closings of the multiple exposure windows overlaps another closing of the multiple exposure windows and determining a difference between an indication of an amount of light captured at the camera during the first exposure window and each other exposure window of the multiple exposure windows.

Systems and methods for determining object range

A light beam scanning device includes a controller device which dynamically adjusts a divergence of the beam. Divergence adjustment can include adjusting the beam divergence along one or more cross sectional axes of the beam. Beam divergence can be adjusted between consecutive scans, during a scan, etc. Beam divergence can be adjusted based on the field of view and scan rate. Beam divergence adjustment can enable dynamic adjustment of the spot size of the beam, which can enable the apparatus to adjust between scanning a wide divergence beam to detect objects in a scene and scanning a narrow divergence beam to generate detailed point clouds of the detected objects. Beam divergence adjustment can enable adjustment of reflection point intensity, enabling detection of low-reflectivity objects.

Dynamic beam spot size for light beam scanning device

An electronic device may have light-based components. The light-based components may include light sources, light detectors, and image sensors. The light-based components may be aligned with a window in the device. The window may be formed within an inactive area of a display or within other device structures. The window may have one or more window members mounted within an opening in a display layer in the inactive area. Visible light blocking material such as chalcogenide glass may be incorporated into the window to provide the window with an opaque appearance that matches the opaque appearance of surrounding portions of the inactive portion of the display. In configurations in which the light-based components include a visible image sensor or other visible light detecting component, the window may be provided with a portion that is transparent at visible wavelengths.

Display with windows for visible and infrared components

A system and method for estimating an ambient light condition is using an image sensor of a digital camera. An array of pixels is obtained using the image sensor. A matrix of grid elements is defined. Each grid element comprises multiple adjacent pixels of the array of pixels. A first measurement value is generated for a grid element of the matrix of grid elements based on the pixels associated with the grid element. A set of grid elements are identified having a first measurement value that satisfies a brightness criteria. A second measurement is generated using the identified set of grid elements. A simulated-light-sensor array is generated using the second measurement value. An estimate of the ambient light condition is calculated using the simulated-light-sensor array.

System and method for estimating an ambient light condition using an image sensor and field-of-view compensation

A system and method for calculating an ambient light estimate using an image sensor in a camera of a device. An array of pixels is obtained using the image sensor. A matrix of grid elements is defined. Each grid element is comprised of multiple adjacent pixels of the array of pixels. A first measurement value is generated for a grid element of the matrix of grid elements based on the pixels associated with a respective grid element. A set of grid elements are identified having a first measurement value that satisfies a brightness criteria. A weighted measurement value is calculated using the identified set of grid elements. The ambient light estimate is calculated based on the weighted measurement value and the first measurement value.

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San Francisco, CA, United States of America

Micah P Kalscheur