Magic Leap, Inc.

Blue River Technology Inc.

Deere + Company

Divya Sharma

Ali Shahrokni

Anush Mohan

Prateek Singhal

Xuan Zhao

Jeremy D Krantz

Tyler Niday

Robert Joseph Plumeau

Michael Albert Elcano

Byron Gajun Ho

Lei Huang

Ashwin Swaminathan

Jean-Yves Bouguet

Etienne Gregoire Grossmann

Dennis William Strelow

Benjamin Langmann

Evan Gregory Levine

Sergiu Sima

Adam Harmat

Yu-Tseh Chi

Lee Kamp Redden

James Patrick Ostrowski

Benjamin Ray Chostner

Kent Michael Anderson

Bryon William Majusiak

Etienne Gregoire Grossman

A vehicle moves through an environment (e.g., a farming, construction, mining, or forestry environment) and performs one or more actions in the environment. Portions of the environment may include moisture, such as puddles or mud patches. A control system associated with the vehicle may include a traversability model or a moisture model to help the vehicle operate in the environment with the moisture. In particular, the control system may employ the traversability model to reduce the likelihood of the vehicle attempting to traverse an untraversable portion of the environment, and the control system may employ the moisture model to reduce the likelihood of the vehicle performing an action that will damage a portion of the environment.

Detecting untraversable environment and preventing damage by a vehicle

An apparatus configured to be worn on a head of a user, includes: a screen configured to present graphics to the user; a camera system configured to view an environment in which the user is located; and a processing unit configured to determine a map based at least in part on output(s) from the camera system, wherein the map is configured for use by the processing unit to localize the user with respect to the environment; wherein the processing unit of the apparatus is also configured to obtain a metric indicating a likelihood of success to localize the user using the map, and wherein the processing unit is configured to obtain the metric by computing the metric or by receiving the metric.

Methods and apparatuses for determining and/or evaluating localizing maps of image display devices

A detection system detects malfunctions in an autonomous farming vehicle during an autonomous routine using one or more models and data from sensors coupled to the autonomous farming vehicle. The models may include machine-learned models trained on the sensor data and configured to identify objects indicative of an operational or malfunctioning component within a tilling assembly such as a tilling shank or sweep. Additionally, a machine-learned model may be trained on sensor data to detect whether debris has plugged the tilling assembly of the autonomous farming vehicle. In response to detecting a malfunction or a plug, the detection system may modify the autonomous routine (e.g., pausing operation) or provide information for the malfunction to be addressed (e.g., the likely location of a malfunctioning sweep that has detached from the tilling assembly).

Machine-learned tillage shank malfunction in an autonomous farming vehicle

Machine-learned tillage sweep malfunction in an autonomous farming vehicle

Machine-learned tillage plug detection in an autonomous farming vehicle

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.

Calibration of augmented reality device

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.

Method for calibrating an augmented reality device

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San Jose, CA, United States of America

Divya Sharma