Apple Inc.

Kuen-Han Lin

Oleg Naroditsky

Yanghai Tsin

Seyed Hesameddin Najafi Shoushtari

Abdelhamid Dine

Stergios I Roumeliotis

Daniel C Byrnes

Dimitrios Kottas

Connie Wu

Various implementations disclosed herein include devices, systems, and methods that localize a device based on detecting planes in depth data acquired by the device. For example, an example process may include detecting first plane data in first sensor data acquired by a sensor at a first viewpoint location in a physical environment, detecting second plane data in second sensor data acquired by the sensor at a second viewpoint location in the physical environment, determining that the first plane data and the second plane data correspond to a same plane based on comparing the first plane data with the second plane data, and determining a spatial transformation between the first viewpoint location and the second viewpoint location based on the first plane data and the second plane data.

Plane extraction and localization

Implementations of the subject technology provide extended reality display devices that can be used on and/or off of a moving platform. Systems and methods are disclosed for separating out the motion of the moving platform from other motions of the device so that virtual content can be displayed without erroneous motions caused by the motion of the moving platform. The subject technology can provide extended reality settings on any suitable moveable platform such as in a car, a watercraft, an aircraft, a train, or any other vehicle.

Inertial data management for extended reality for moving platforms

Methods and apparatus for glint-assisted gaze tracking in a VR/AR head-mounted display (HMD). Images of a user's eyes captured by gaze tracking cameras may be analyzed to detect glints (reflections on the cornea of light sources that illuminate the user's eyes) and the pupil. The glints are matched to particular ones of the light sources. The glint-light source matches are used to determine the cornea center of the eye, and the pupil center is determined. The optical axis of the eye is reconstructed from the cornea center and the pupil center, and the visual axis is then reconstructed from the optical axis and a 3D model of the user's eye. The point of gaze on the display is then determined based on the visual axis and a 3D model of the HMD.

Glint-assisted gaze tracker

Systems, methods, and computer readable media to track and estimate the accuracy of a visual inertial odometry (VIO) system. Various embodiments are able to receive one or more VIO feature measurements associated with a set of image frames from a VIO system and generate a plurality of feature models to estimate health values for the VIO system. The various embodiments determine a plurality of feature health values with the feature models based on the VIO feature measurements and compare the feature health values with ground truth health scores associated with the set of image frames to determine one or more errors. The feature model parameters are updated based on the comparison with the feature health values with ground truth health scores.

Visual inertial odometry health fitting

In some implementations, a first electronic device including a first image sensor uses a processor to perform a method. The method involves obtaining a first set of keyframes based on images of a physical environment captured by the first image sensor. The method generates a mapping defining relative locations of keyframes of the first set of keyframes. The method receives a keyframe corresponding to an image of the physical environment captured at a second, different electronic device and localizes the received keyframe to the mapping. The method then receives an anchor from the second electronic device that defines a position of a virtual object relative to the keyframe. The method displays a CGR environment including the virtual object at a location based on the anchor and the mapping.

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Mountain View, CA, United States of America

Kuen-Han Lin