Magic Leap, Inc.

Nio Usa, Inc.

Omid Ghasemalizadeh

Ashwin Swaminathan

Martin Georg Zahnert

Dominik Michael Kasper

Joao Antonio Pereira Faro

Benjamin Langmann

Sergiu Sima

Evan Gregory Levine

Igor Napolskikh

Christoph Tobler

Yu-Hsiang Huang

Manel Quim Sanchez Nicuesa

Blazej Marek Czuprynski

Arnau Franci Rodon

Albert Wu

Zhongkui Wang

João Antonio Pereira Faro

Manuel Quim Sanchez Nicuesa

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.

Determining angular acceleration

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.

Dual IMU SLAM

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.

Gravity estimation and bundle adjustment for visual-inertial odometry

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.

Vehicle control system using nonlinear dynamic model states and steering offset estimation

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San Jose, CA, United States of America

Omid Ghasemalizadeh