Google Inc.

University of Minnesota

Meta Platforms, Inc.

Google Technology Holdings LLC

Joel Hesch

Johnny Hsienchow Lee

James Fung

Esha D Nerurkar

Patrick Mihelich

Ryan Michael Hickman

Stergios I Roumeliotis

Chao X Guo

Simon Lynen

Johnny Chung Lee

Rahul Garg

Ashish A Shah

Georgios Georgiou

Shiqi Chen

Boyang Zhang

Dimitrios G Kottas

Kourosh Sartipi

Ryan C DuToit

Zachary Moratto

Mingyang Li

Esha Nerurkar

A platform system receives sensor data describing the state and orientation of a tracked object and models the pose of the tracked object to determine user interactions with the platform system. To ensure that incorrect sensor data due to a saturation event or connection loss does not impact user experience, the platform system identifies regions for correction in sensor data streams based on the sensor data being at or above a saturation limit or not being received. The platform system predicts sensor data for an identified region of correction by applying a fit corresponding to points adjacent to the region for correction and determining predicted sensor data using the applied fit. The predicted sensor data is used to correct the modeled pose for the tracked object.

Modeling poses of tracked objects by predicting sensor data

An electronic device includes one or more imaging cameras. After a reset of the device or other specified event, the electronic device identifies an estimate of the device's pose based on location data such as Global Positioning System (GPS) data, cellular tower triangulation data, wireless network address location data, and the like. The one or more imaging cameras may be used to capture imagery of the local environment of the electronic device, and this imagery is used to refine the estimated pose to identify a refined pose of the electronic device. The refined pose may be used to identify additional imagery information, such as environmental features, that can be used to enhance the location based functionality of the electronic device.

Electronic device localization based on imagery

Fault detection for real-time visual-inertial odometry motion tracking. A fault detection system allows immediate detection of error when the motion of a device cannot be accurately determined. The system includes subdetectors that operate independently and in parallel to a main system on a device to determine if a condition exists which results in a main system error. Each subdetector covers a phase of a six-degrees of freedom (6DOF) estimation. If any of the subdetectors detect an error, a fault is output to the main system to indicate a motion tracking failure.

Real-time visual-inertial motion tracking fault detection

A method for controller tracking with multiple degrees of freedom includes generating depth data at an electronic device based on a local environment proximate the electronic device. A set of positional data is generated for at least one spatial feature associated with a controller based on a pose of the electronic device, as determined using the depth data, relative to the at least one spatial feature associated with the controller. A set of rotational data is received that represents three degrees-of-freedom (3DoF) orientation of the controller within the local environment, and a six degrees-of-freedom (6DoF) position of the controller within the local environment is tracked based on the set of positional data and the set of rotational data.

Controller tracking for multiple degrees of freedom

A head mounted display (HMD) adjusts feature tracking parameters based on a power mode of the HMD. Examples of feature tracking parameters that can be adjusted include the number of features identified from captured images, the scale of features identified from captured images, the number of images employed for feature tracking, and the like. By adjusting its feature tracking parameters based on its power mode, the HMD can initiate the feature tracking process in low-power modes and thereby shorted the time for high-fidelity feature tracking when a user initiates a VR or AR experience at the HMD.

Low-power mode feature identification at a head mounted display

An electronic device includes at least one sensor, a display, and a processor. The processor is configured to determine a dimension of a physical object along an axis based on a change in position of the electronic device when the electronic device is moved from a first end of the physical object along the axis to a second end of the physical object along the axis. A method includes capturing and displaying imagery of a physical object at an electronic device, and receiving user input identifying at least two points of the physical object in the displayed imagery. The method further includes determining, at the electronic device, at least one dimensional aspect of the physical object based on the at least two points of the physical object using a three-dimensional mapping of the physical object.

Measuring dimension of object through visual odometry

A method includes: receiving, with a computing platform, respective trajectory data and map data independently generated by each of a plurality of vision-aided inertial navigation devices (VINS devices) traversing an environment, wherein the trajectory data specifies poses along a path through the environment for the respective VINS device and the map data specifies positions of observed features within the environment as determined by an estimator executed by the respective VINS device; determining, with the computing platform and based on the respective trajectory data and map data from each of the VINS devices, estimates for relative poses within the environment by determining transformations that geometrically relate the trajectory data and the map data between one or more pairs of the VINS devices; and generating, with the computing platform and based on the transformations, a composite map specifying positions within the environment for the features observed by the VINS devices.

Resource-aware large-scale cooperative 3D mapping using multiple mobile devices

An electronic device balances gain and exposure at an imaging sensor of the device based on detected image capture conditions, such as motion of the electronic device, distance of a scene from the electronic device, and predicted illumination conditions for the electronic device. By balancing the gain and exposure, the quality of images captured by the imaging sensor is enhanced, which in turn provides for improved support of location-based functionality.

Balancing exposure and gain at an electronic device based on device motion and scene distance

An electronic device includes one or more imaging sensors (e.g, imaging cameras) and includes one or more non-image sensors, such as an inertial measurement unit (IMU), that can provide information indicative of the pose of the electronic device. The electronic device estimates its pose based on two independent sources of pose information: pose information generated at a relatively high rate based on non-visual information generated by the non-image sensors and pose information generated at a relatively low rate based on imagery captured by the one or more imaging sensors. To achieve both a high pose-estimation rate and high degree of pose estimation accuracy, the electronic device adjusts a pose estimate based on the non-visual pose information at a high rate, and at a lower rate spatially smoothes the pose estimate based on the visual pose information.

Electronic device pose identification based on imagery and non-image sensor data

Methods and systems for determining features of interest for following within various frames of data received from multiple sensors of a device are disclosed. An example method may include receiving data from a plurality of sensors of a device. The method may also include determining, based on the data, motion data that is indicative of a movement of the device in an environment. The method may also include as the device moves in the environment, receiving image data from a camera of the device. The method may additionally include selecting, based at least in part on the motion data, features in the image data for feature-following. The method may further include estimating one or more of a position of the device or a velocity of the device in the environment as supported by the data from the plurality of sensors and feature-following of the selected features in the images.

Odometry feature matching

An electronic device reduces localization data based on feature characteristics identified from the data. Based on the feature characteristics, a quality value can be assigned to each identified feature, indicating the likelihood that the data associated with the feature will be useful in mapping a local environment of the electronic device. The localization data is reduced by removing data associated with features have a low quality value, and the reduced localization data is used to map the local environment of the device by locating features identified from the reduced localization data in a frame of reference for the electronic device.

Managing feature data for environment mapping on an electronic device

Methods and systems for acquiring sensor data using multiple acquisition modes are described. An example method involves receiving, by a co-processor and from an application processor, a request for sensor data. The request identifies at least two sensors of a plurality of sensors for which data is requested. The at least two sensors are configured to acquire sensor data in a plurality of acquisition modes, and the request further identifies for the at least two sensors respective acquisition modes for acquiring data that are selected from among the plurality of acquisition modes. In response to receiving the request, the co-processor causes the at least two sensors to acquire data in the respective acquisition modes. The co-processor receives first sensor data from a first sensor and second sensor data from a second sensor, and the co-processor provides the first sensor data and the second sensor data to the application processor.

Methods and systems for acquiring sensor data on a device using multiple acquisition modes

Methods and systems for communicating sensor data on a mobile device are described. An example method involves receiving, by a processor and from an inertial measurement unit (IMU), sensor data corresponding to a first timeframe, and storing the sensor data using a data buffer. The processor may also receive image data and sensor data corresponding to a second timeframe. The processor may then generate a digital image that includes at least the image data corresponding to the second timeframe and the sensor data corresponding to the first timeframe and the second timeframe. The processor may embed the stored sensor data corresponding to the first timeframe and the second timeframe in pixels of the digital image. And the processor may provide the digital image to an application processor of the mobile device.

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Mountain View, CA, United States of America

Joel Hesch