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: Disclosed herein are systems and methods for calculating angular acceleration based on inertial data using two or more inertial measurement units (IMUs). The calculated angular acceleration may be used to estimate a position of a wearable head device comprising the IMUs. Virtual content may be presented based on the position of the wearable head device. In some embodiments, a first IMU and a second IMU share a coincident measurement axis.

Abstract: Disclosed herein are systems and methods for calculating angular acceleration based on inertial data using two or more inertial measurement units (IMUs). The calculated angular acceleration may be used to estimate a position of a wearable head device comprising the IMUs. Virtual content may be presented based on the position of the wearable head device. In some embodiments, a first IMU and a second IMU share a coincident measurement axis.

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: Disclosed herein are systems and methods for calculating angular acceleration based on inertial data using two or more inertial measurement units (IMUs). The calculated angular acceleration may be used to estimate a position of a wearable head device comprising the IMUs. Virtual content may be presented based on the position of the wearable head device. In some embodiments, a first IMU and a second IMU share a coincident measurement axis.

Abstract: Methods and systems for autonomously steering a moving vehicle are disclosed. A processor determines a longitudinal velocity, a longitudinal acceleration, a lateral acceleration, and a yaw rate of the vehicle. The processor estimates, based on the longitudinal velocity, lateral acceleration, and yaw rate of the vehicle, a change in lateral velocity over time. The processor estimates, based on the change in the lateral velocity over time, the yaw rate, a distance between the front axle of the vehicle and a center of gravity of the vehicle, and a distance between the rear axle of the vehicle and the center of gravity of the vehicle, a lateral front velocity of the vehicle and a lateral rear velocity of the vehicle. Using calculations, a state estimation model for the vehicle is updated by the processor using a lateral acceleration bias. The updated state estimation model is used to autonomously steer the vehicle.

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: Methods and systems for autonomously steering a moving vehicle are disclosed. A processor determines a longitudinal velocity, a longitudinal acceleration, a lateral acceleration, and a yaw rate of the vehicle. The processor estimates, based on the longitudinal velocity, lateral acceleration, and yaw rate of the vehicle, a change in lateral velocity over time. The processor estimates, based on the change in the lateral velocity over time, the yaw rate, a distance between the front axle of the vehicle and a center of gravity of the vehicle, and a distance between the rear axle of the vehicle and the center of gravity of the vehicle, a lateral front velocity of the vehicle and a lateral rear velocity of the vehicle. Using calculations, a state estimation model for the vehicle is updated by the processor using a lateral acceleration bias. The updated state estimation model is used to autonomously steer the vehicle.