Abstract: Examples of the disclosure describe systems and methods for presenting virtual content on a wearable head device. In some embodiments, a state of a wearable head device is determined by minimizing a total error based on a reduced weight associated with a reprojection error. A view reflecting the determined state of the wearable head device is presented via a display of the wearable head device. In some embodiments, a wearable head device calculates a preintegration term based on the image data received via a sensor of the wearable head device and the inertial data received via a first IMU and a second IMU of the wearable head device. The wearable head device estimates a position of the device based on the preintegration term, and the wearable head device presents the virtual content based on the position of the device.

Abstract: A wearable computing device, including a device body, an inertial measurement unit (IMU), and a processor. The processor may receive, from the IMU, a plurality of kinematic measurements collected within a time window. With one or more machine learning models, based at least in part on the kinematic measurements, the processor may compute a current velocity estimate for the wearable computing device at a current timestep and a prior velocity estimate for the wearable computing device at a prior timestep. The processor may compute a current pose estimate and a prior pose estimate based at least in part on the current velocity estimate and the prior velocity estimate, respectively. The processor may compute a composite pose estimate for the wearable computing device at the current timestep based on the current pose estimate and the prior pose estimate. The processor may output the composite pose estimate to a target program.

Abstract: A wearable computing device, including a device body configured to be affixed to a body of a user. The wearable computing device may further include an inertial measurement unit (IMU) and a processor. The processor may receive kinematic data from the IMU while the device body is affixed to the body of the user. The processor may perform a first coordinate transformation on the kinematic data into a training coordinate frame of a training wearable computing device. At a first machine learning model trained using training data including training kinematic data collected at the training wearable computing device, the processor may compute a training-frame velocity estimate for the wearable computing device based on the transformed kinematic data. The processor may perform a second coordinate transformation on the training-frame velocity estimate to obtain a runtime-frame velocity estimate and may output the runtime-frame velocity estimate to a target program.

Abstract: A wearable computing device, including a device body, an inertial measurement unit (IMU), and a processor. The processor may receive, from the IMU, a plurality of kinematic measurements collected within a time window. With one or more machine learning models, based at least in part on the kinematic measurements, the processor may compute a current velocity estimate for the wearable computing device at a current timestep and a prior velocity estimate for the wearable computing device at a prior timestep. The processor may compute a current pose estimate and a prior pose estimate based at least in part on the current velocity estimate and the prior velocity estimate, respectively. The processor may compute a composite pose estimate for the wearable computing device at the current timestep based on the current pose estimate and the prior pose estimate. The processor may output the composite pose estimate to a target program.

Abstract: A method of automatically geolocating a visual target. The method comprises operating a flying vehicle in a search region including the visual target. The method further includes affirmatively identifying a visual target in an aerial photograph of the search region captured by the flying vehicle. The method further includes automatically correlating the aerial photograph of the search region to a geo-tagged photograph of the search region, wherein the geo-tagged photograph is labelled with pre-defined geospatial coordinates. Based on such automatic correlation, a geospatial coordinate is determined for the visual target in the search region.

Abstract: One method comprises receiving a hit signal from a device worn by a first player, receiving a position of the device, receiving an orientation of a launch axis of a virtual-projectile launcher, receiving a position of a second player, and outputting a hit assignment on determining, pursuant to receiving the hit signal, that a recognized object and the second player are coincident at an indicated launch of a virtual projectile. Another method comprises receiving an indication of launch of a virtual projectile by a virtual-projectile launcher of a first player, receiving an image aligned to a launch axis of the virtual-projectile launcher, outputting a hit signal to a server on determining, pursuant to receiving the indication of launch, that a recognized object is imaged in a projectile-delivery area of the image, and outputting a position of the device and an orientation of the launch axis to the server.

Abstract: A wearable computing device, including a device body configured to be affixed to a body of a user. The wearable computing device may further include an inertial measurement unit (IMU) and a processor. The processor may receive kinematic data from the IMU while the device body is affixed to the body of the user. The processor may perform a first coordinate transformation on the kinematic data into a training coordinate frame of a training wearable computing device. At a first machine learning model trained using training data including training kinematic data collected at the training wearable computing device, the processor may compute a training-frame velocity estimate for the wearable computing device based on the transformed kinematic data. The processor may perform a second coordinate transformation on the training-frame velocity estimate to obtain a runtime-frame velocity estimate and may output the runtime-frame velocity estimate to a target program.

Abstract: A wearable computing device, including a device body, an inertial measurement unit (IMU), and a processor. The processor may receive, from the IMU, a plurality of kinematic measurements collected within a time window. With one or more machine learning models, based at least in part on the kinematic measurements, the processor may compute a current velocity estimate for the wearable computing device at a current timestep and a prior velocity estimate for the wearable computing device at a prior timestep. The processor may compute a current pose estimate and a prior pose estimate based at least in part on the current velocity estimate and the prior velocity estimate, respectively. The processor may compute a composite pose estimate for the wearable computing device at the current timestep based on the current pose estimate and the prior pose estimate. The processor may output the composite pose estimate to a target program.

Abstract: Systems are provided for estimating 6DOF positioning of a computing device while in a pedestrian dead reckoning mode. The systems obtain a set of inertial tracking data from the set of one or more inertial tracking components while the system is in a pedestrian dead reckoning mode. Then, the systems obtain an estimated 3DOF velocity of the system based inertial tracking data, using a predictive model trained on a set of observed exteroceptive sensor data and observed inertial tracking data. The systems also obtain estimated 6DOF positioning of the systems based on the estimated 3DOF velocity.

Abstract: Examples of the disclosure describe systems and methods for presenting virtual content on a wearable head device. In some embodiments, a state of a wearable head device is determined by minimizing a total error based on a reduced weight associated with a reprojection error. A view reflecting the determined state of the wearable head device is presented via a display of the wearable head device. In some embodiments, a wearable head device calculates a preintegration term based on the image data received via a sensor of the wearable head device and the inertial data received via a first IMU and a second IMU of the wearable head device. The wearable head device estimates a position of the device based on the preintegration term, and the wearable head device presents the virtual content based on the position of the device.

Abstract: Examples of the disclosure describe systems and methods for presenting virtual content on a wearable head device. In some embodiments, a state of a wearable head device is determined by minimizing a total error based on a reduced weight associated with a reprojection error. A view reflecting the determined state of the wearable head device is presented via a display of the wearable head device. In some embodiments, a wearable head device calculates a first preintegration term and second preintegration term based on the image data received via a sensor of the wearable head device and the inertial data received via a first IMU and a second IMU of the wearable head device. The wearable head device estimates a position of the device based on the first and second preintegration terms, and the wearable head device presents the virtual content based on the position of the device.

Abstract: Techniques are described for calibrating a device having a first sensor and a second sensor. Techniques include capturing sensor data using the first sensor and the second sensor. The device maintains a calibration profile including a translation parameter and a rotation parameter to model a spatial relationship between the first sensor and the second sensor. Techniques include determining a calibration level associated with the calibration profile at a first time. Techniques include determining, based on the calibration level, to perform a calibration process. Techniques include performing the calibration process at the first time by generating one or both of a calibrated translation parameter and a calibrated rotation parameter and replacing one or both of the translation parameter and the rotation parameter with one or both of the calibrated translation parameter and the calibrated rotation parameter.

Abstract: One method comprises receiving a hit signal from a device worn by a first player, receiving a position of the device, receiving an orientation of a launch axis of a virtual-projectile launcher, receiving a position of a second player, and outputting a hit assignment on determining, pursuant to receiving the hit signal, that a recognized object and the second player are coincident at an indicated launch of a virtual projectile. Another method comprises receiving an indication of launch of a virtual projectile by a virtual-projectile launcher of a first player, receiving an image aligned to a launch axis of the virtual-projectile launcher, outputting a hit signal to a server on determining, pursuant to receiving the indication of launch, that a recognized object is imaged in a projectile-delivery area of the image, and outputting a position of the device and an orientation of the launch axis to the server.

Abstract: A computer device is provided that a processor configured to determine a plurality of candidate heading and velocity values from an initial position based on at least on measurements from an inertial measurement unit and a compass device. The processor is further configured to determine a probability for each of the plurality of candidate heading and velocity values using a probabilistic framework that assigns a lower probability to candidate heading and velocity values that conflict with travel constraining map features. The processor is further configured to rank the plurality of candidate heading and velocity values and track a position for the computer device based on a highest ranked candidate heading and velocity value.

Abstract: A head-mounted display device includes a near-eye display configured to present virtual imagery. A storage machine holds instructions executable by a logic machine to concurrently output first and second position estimates via first and second navigation modalities of the device. Based on determining that the first position estimate has a higher confidence value than the second position estimate, the first position estimate is reported. As the device moves away from the first reported position, first and second subsequent position estimates are concurrently output. Based on determining that the second subsequent position estimate has a higher confidence value than the first subsequent position estimate, the second subsequent position estimate is reported. Position-specific virtual imagery is presented to a user eye via the near-eye display, the position-specific virtual imagery dynamically updating as the head-mounted display device moves.

Abstract: A method of automatically geolocating a visual target. The method comprises operating a flying vehicle in a search region including the visual target. The method further includes affirmatively identifying a visual target in an aerial photograph of the search region captured by the flying vehicle. The method further includes automatically correlating the aerial photograph of the search region to a geo-tagged photograph of the search region, wherein the geo-tagged photograph is labelled with pre-defined geospatial coordinates. Based on such automatic correlation, a geospatial coordinate is determined for the visual target in the search region.

Abstract: Systems are provided for estimating 6DOF positioning of a computing device while in a pedestrian dead reckoning mode. The systems obtain a set of inertial tracking data from the set of one or more inertial tracking components while the system is in a pedestrian dead reckoning mode. Then, the systems obtain an estimated 3DOF velocity of the system based inertial tracking data, using a predictive model trained on a set of observed exteroceptive sensor data and observed inertial tracking data. The systems also obtain estimated 6DOF positioning of the systems based on the estimated 3DOF velocity.

Abstract: Examples of the disclosure describe systems and methods for presenting virtual content on a wearable head device. In some embodiments, a state of a wearable head device is determined by minimizing a total error based on a reduced weight associated with a reprojection error. A view reflecting the determined state of the wearable head device is presented via a display of the wearable head device. In some embodiments, a wearable head device calculates a first preintegration term and second preintegration term based on the image data received via a sensor of the wearable head device and the inertial data received via a first IMU and a second IMU of the wearable head device. The wearable head device estimates a position of the device based on the first and second preintegration terms, and the wearable head device presents the virtual content based on the position of the device.

Abstract: Techniques are described for calibrating a device having a first sensor and a second sensor. Techniques include capturing sensor data using the first sensor and the second sensor. The device maintains a calibration profile including a translation parameter and a rotation parameter to model a spatial relationship between the first sensor and the second sensor. Techniques include determining a calibration level associated with the calibration profile at a first time. Techniques include determining, based on the calibration level, to perform a calibration process. Techniques include performing the calibration process at the first time by generating one or both of a calibrated translation parameter and a calibrated rotation parameter and replacing one or both of the translation parameter and the rotation parameter with one or both of the calibrated translation parameter and the calibrated rotation parameter.

Abstract: A method for calibrating a device having a first sensor and a second sensor. The method includes capturing sensor data using the first sensor and the second sensor. The device maintains a calibration profile including a translation parameter and a rotation parameter to model a spatial relationship between the first sensor and the second sensor. The method also includes determining a calibration level associated with the calibration profile at a first time. The method further includes determining, based on the calibration level, to perform a calibration process. The method further includes performing the calibration process at the first time by generating one or both of a calibrated translation parameter and a calibrated rotation parameter and replacing one or both of the translation parameter and the rotation parameter with one or both of the calibrated translation parameter and the calibrated rotation parameter.