Snap Inc.

Georg Halmetschlager-Funek

Matthias Kalkgruber

Daniel Wolf

Clemens Birklbauer

Jeroen Diederik Hol

Jakob Zillner

Daniel Wagner

Olha Borys

Kai Zhou

Nikolaj Kuntner

Tiago Miguel Pereira Torres

Simon Schreiberhuber

A method for recalibrating an augmented reality (AR) device includes generating and storing a ground truth map of a real-world environment when the AR device is operating with a high likelihood of having an accurate factory calibration. During operation of the AR device, new map data is generated for the real-world environment. The new map data is compared to the ground truth map to detect potential calibration errors. If calibration errors are detected, a recalibration procedure is executed by determining an optimal path through the real-world environment that allows for observing parameters requiring recalibration. Visual cues are generated to guide a user of the AR device through the optimal path. As the user follows the visual cues, calibration parameters are iteratively adjusted to eliminate detected calibration errors. The recalibration procedure may be presented as an interactive game to improve user engagement, with rewards provided for accurately following guidance.

Field calibration of an augmented reality device

An eyewear device with flexible frame for Augmented Reality (AR) is disclosed. At least two sensors and a display are mounted on the flexible frame. When in use, the real time geometry of the eyewear device may change from factory calibrated geometry, resulting in low quality AR rendering. A modeling module is provided to model the real time geometry of the eyewear device on the fly using sensor information of the at least two sensors. The modeled real time geometry is then provided to a rendering module to accurately display the AR to the user.

Geometry modeling of eyewear devices with flexible frames

Visual-inertial tracking of an eyewear device using sensors. The eyewear device monitors the sensors of a visual inertial odometry system (VIOS) that provide input for determining a position of the device within its environment. The eyewear device determines the status of the VIOS based information from the sensors and adjusts the plurality of sensors (e.g., by turning on/off sensors, changing the sampling rate, of a combination thereof) based on the determined status. The eyewear device then determines the position of the eyewear device within the environment using the adjusted plurality of sensors.

Dynamic sensor selection for visual inertial odometry systems

A method for calibrating a visual-inertial tracking system is described. A device operates the visual-inertial tracking system without receiving a tracking request from a virtual object display application. In response to operating the visual-inertial tracking system, the device accesses sensor data from sensors at the device. The device identifies, based on the sensor data, a first calibration parameter value of the visual-inertial tracking system and stores the first calibration parameter value. The system detects a tracking request from the virtual object display application. In response to the tracking request, the system accesses the first calibration parameter value and determines a second calibration parameter value from the first calibration parameter value.

Periodic parameter estimation for visual-inertial tracking systems

Disclosed are systems, methods, and non-transitory computer-readable media for misaligned vantage point mitigation for computer stereo vision. A misaligned vantage point mitigation system determines whether vantage points of the optical sensors are misaligned from an expected vantage point and, if so, determines an adjustment variable to mitigate the misalignment based on the location of matching features identified in images captured by both optical sensors.

Misaligned vantage point mitigation for computer stereo vision

A method and apparatus of tracking poses of a rolling-shutter camera in an augmented reality (AR) system is provided. The method and apparatus use camera information and inertial sensor readings from Inertial Measurement Unit (IMU) to estimate the pose of the camera at a reference line. Thereafter, relative pose changes at scanlines may be calculated using the inertial sensor data. The estimated reference pose of the camera is then further refined based on the visual information from the camera, the relative pose changes and the optimized reference line pose of a previous image. Thereafter, the estimate of the scanline poses may be updated using the relative pose changes obtained in the earlier steps.

Pose tracking for rolling shutter camera

A method for adjusting camera intrinsic parameters of a multi-camera visual tracking device is described. In one aspect, a method for calibrating the multi-camera visual tracking system includes disabling a first camera of the multi-camera visual tracking system while a second camera of the multi-camera visual tracking system is enabled, detecting a first set of features in a first image generated by the first camera after detecting that the temperature of the first camera is within the threshold of the factory calibration temperature of the first camera, and accessing and correcting intrinsic parameters of the second camera based on the projection of the first set of features in the second image and a second set of features in the second image.

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Vienna, Austria

Georg Halmetschlager-Funek