Embark Trucks Inc.

The Boeing Company

Phillip James Haeusler

Grady D Williams

Alexandre Desbiez

Dominic Wierzbicki

Martin Szarski

Martin Alexander Szarski

Andrew K Glynn

Richard Bain

David Michael Bain

Peter Nathan Steele

Martin A Szarski

A system and method including receiving a digital representation of a road that includes one or more lane representations; defining a factor graph including variable nodes and constraint nodes that encode lane boundary constraints for the lane map; translating the factor graph into a nonlinear optimization problem for altitudes of the lane map; generating a solution to the optimization problem; and generating a corrected lane map with an optimized altitude for the road based on the generated solution.

Altitude smoothing

Provided are systems and methods for detecting a vehicle with sensors that are not calibrated properly and calibrating such sensor in real-time. In one example, a method may include iteratively capturing sensor data of a road while the vehicle is travelling on the road; monitoring a calibration of the sensors of the vehicle based on the sensor data, determining that the sensors of the vehicle are not calibrated properly based on the monitoring, generating a calibration target of an object on the road based on the sensor data, and adjusting a calibration parameter of the one or more sensors of the vehicle based on the generated calibration target.

Automated real-time calibration

In an example, a method for calibrating fiducials relative to a workpiece is disclosed and involves receiving, from one or more cameras, images of the fiducials, each image acquired from a different camera pose, where the fiducials comprise (i) one or more first fiducials fixed at one or more unknown locations on the workpiece and (ii) calibration fiducials disposed at known locations on the workpiece; estimating poses of the fiducials, where estimating the poses of the fiducials comprises, in each image, estimating, for each fiducial visible in the image, a pose of the fiducial relative to the camera(s); formulating a pose graph comprising nodes for the images and for the fiducials, the poses estimated for the fiducials, the calibration fiducials as landmarks with known poses, and constraints between the calibration fiducials and the images; and solving the pose graph to determine poses of the first fiducial(s) that satisfy the constraints.

Methods and systems for calibrating fiducial markers relative to an object

Provided are systems and methods for auto calibrating a vehicle using a calibration target that is generated from the vehicle's sensor data. In one example, the method may include receiving sensor data associated with a road captured by one or more sensors of a vehicle, identifying lane line data points within the sensor data, generating a representation which includes positions of a plurality of lane lines of the road based on the identified lane line data points, and adjusting a calibration parameter of a sensor from among the one or more sensors of the vehicle based on the representation of the plurality of lane lines.

Automatic extrinsic calibration using sensed data as a target

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San Francisco, CA, United States of America

Phillip James Haeusler