The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.

The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.

Date of Patent:
Mar. 30, 2021

Filed:

Oct. 18, 2019
Applicants:

Toyota Research Institute, Inc., Los Altos, CA (US);

The Regents of the University of Michigan, Ann Arbor, MI (US);

Inventors:

Jeffrey M. Walls, Ann Arbor, MI (US);

Steven A. Parkison, Ann Arbor, MI (US);

Ryan W. Wolcott, Ann Arbor, MI (US);

Ryan M. Eustice, Ann Arbor, MI (US);

Assignees:
Attorneys:
Primary Examiner:
Int. Cl.
CPC ...
G06T 7/80 (2017.01); G01S 7/497 (2006.01); G01S 17/42 (2006.01); G01S 17/86 (2020.01);
U.S. Cl.
CPC ...
G01S 7/4972 (2013.01); G01S 17/42 (2013.01); G01S 17/86 (2020.01); G06T 7/80 (2017.01);
Abstract

A system and method for calibrating sensors may include one or more processors, a first sensor configured to obtain a two-dimensional image, a second sensor configured to obtain three-dimensional point cloud data, and a memory device. The memory device stores a data collection module and a calibration module. The data collection module has instructions that configure the one or more processors to obtain the two-dimensional image and the three-dimensional point cloud data. The calibration module has instructions that configure the one or more processors to determine and project a three-dimensional point cloud edge of the three-dimensional point cloud data onto the two-dimensional image edge, apply a branch-and-bound optimization algorithm to a plurality of rigid body transforms, determine a lowest cost transform of the plurality of rigid body transforms using the branch-and-bound optimization algorithm, and calibrate the first sensor with the second sensor using the lowest cost transform.


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