Magic Leap, Inc.

Lyft, Inc.

Alpha Omega Semiconductor Inc.

Woven Planet North America, Inc.

Lei Zhang

Ashesh Jain

Etienne Gregoire Grossmann

Marshall Charles Capps

Kazunori Tanaka

Mushfiqur Rouf

Henru Wang

Yunjian Jiang

Madhur Bobde

Sik Lui

John Chen

Eric C Browy

Robert Blake Taylor

Jongoh Kim

Forrest Samuel Briggs

Jason A Schaefer

Zhiheng Jia

Yongping Ding

Lukas Platinsky

Miller Harry Schuck, Iii

John Monos

Nukul Sanjay Shah

Karim Tarek Mahmoud Elsayed Ahmed Shaban

Grace Vesom

Li Jiang

Nicholas Ihle Morley

Ohad Zohar

Robert Zito

Examples disclosed herein involve a computing system configured to (i) obtain first sensor data captured by a first sensor of a vehicle during a given period of operation of the vehicle (ii) obtain second sensor data captured by a second sensor of the vehicle during the given period of operation of the vehicle, (iii) based on the first sensor data, localize the first sensor within a first coordinate frame of a first map layer, (iv) based on the second sensor data, localize the second sensor within a second coordinate frame of a second map layer, (v) based on a known transformation between the first coordinate frame and the second coordinate frame, determine respective poses for the first sensor and the second sensor in a common coordinate frame, and (vi) determine (a) a translation and (b) a rotation between the respective poses for the first and second sensors in the common coordinate frame.

Validating vehicle sensor calibration

A method for characterizing a digital color camera includes, for each of three primary colors used in a field sequential color virtual image, determining a conversion model for each color using RGB values and the color-measurement values. For each primary color, the method includes illuminating a display device using an input light beam of a primary color having spectral properties representative of a light beam in a virtual image in a wearable device. The method includes capturing, with the digital color camera, an image of the display device, and determining, from the image, RGB values for each primary color. The method includes capturing, with a color-measurement device, a color-measurement value associated with each corresponding primary color at the display device, thereby acquiring a color-measurement value in an absolute color space. A conversion model for each color is determined using RGB values and the color-measurement values.

Method and system for color calibration of an imaging device

Examples disclosed herein may involve a computing system that is configured to (i) obtain previously-derived perception data for a collection of sensor data including a sequence of frames observed by a vehicle within one or more scenes, where the previously-derived perception data includes a respective set of object-level information for each of a plurality of objects detected within the sequence of frames, (ii) derive supplemental object-level information for at least one object detected within the sequence of frames that adds to the previously-derived object-level information for the at least one object, (iii) augment the previously-derived perception data to include the supplemental object-level information for the at least one object, and (iv) store the augmented perception data in an arrangement that encodes a hierarchical relationship between the plurality of objects, the sequence of frames, and the one or more scenes.

Systems and methods for augmenting perception data with supplemental information

A method for calibrating a wearable device includes displaying an image with a plurality of pixels for each of three primary colors using the wearable device, and determining RGB and XYZ values for each of the plurality of pixels. The method includes selecting a subset of the plurality of pixels to form a group of grid points, and dividing the image into a group of tile regions, with each tile region including a grid point. Grid XYZ values are determined for each grid point, based on averaging XYZ values of all pixels in a corresponding tile region, and a grid RGB-to-XYZ conversion matrix is determined for each grid point. The method also includes determining a correction matrix for each grid point by multiplying an inverse of the grid RGB-to-XYZ conversion matrix for the grid with an sRGB-to-XYZ conversion matrix.

Method and system for subgrid calibration of a display device

Systems, methods, and non-transitory computer-readable media can determine first sensor data captured by a first sensor of a vehicle. Second sensor data captured by a second sensor of the vehicle can be determined. Information describing the first sensor data and the second sensor data can be provided to a machine learning model trained to predict whether a pair of sensors are calibrated or mis-calibrated based on sensor data captured by the pair of sensors. A determination is made whether the first sensor and the second sensor are calibrated or mis-calibrated based on an output from the machine learning model.

Approaches for determining sensor calibration

A calibration set is stored on a storage device, including a plurality of temperatures and a plurality of homography transformation matrices, each respective homography transformation matrix being for a respective temperature. A temperature is detected. A respective one of the homography transformation matrices is selected for a temperature matching the temperature. A geometric change is calculated based on the selected homography relationship. Data representing local content is received. A rendering of the local content is generated based on the geometric change and displayed to a user.

Homography transformation matrices based temperature calibration of a viewing system

A method includes capturing, by a plurality of image sensors on an automotive vehicle, image data associated with one or more calibration objects in an environment, and capturing, by a LiDAR sensor, a three-dimensional LiDAR point cloud based on LiDAR data. The method further comprises generating a three-dimensional image point cloud based on the image data and the three-dimensional LiDAR point cloud, mapping a first alignment plane of the three-dimensional image point cloud relative to a second alignment plane of the three-dimensional LiDAR point cloud for each of the calibration objects to determine an angle between the first alignment plane and second alignment plane, and calibrating the LiDAR sensor relative to the image sensors by determining a degree of rotation of the LiDAR sensor to minimize the angle between the first alignment plane and second alignment plane.

LiDAR and camera rotational position calibration using multiple point cloud comparisons

A semiconductor power device disposed on a semiconductor substrate comprises a plurality of trenches formed at a top portion of the semiconductor substrate extending laterally across the semiconductor substrate along a longitudinal direction each having a nonlinear portion comprising a sidewall perpendicular to a longitudinal direction of the trench and extends vertically downward from a top surface to a trench bottom surface. The semiconductor power device further includes a trench bottom dopant region disposed below the trench bottom surface and a sidewall dopant region disposed along the perpendicular sidewall wherein the sidewall dopant region extends vertically downward along the perpendicular sidewall of the trench to reach the trench bottom dopant region and pick-up the trench bottom dopant region to the top surface of the semiconductor substrate.

Process method and structure for high voltage MOSFETS

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Sunnyvale, CA, United States of America

Lei Zhang