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:
Sep. 12, 2023

Filed:

Dec. 28, 2021
Applicant:

Nvidia Corporation, Santa Clara, CA (US);

Inventors:

Lin Yang, San Carlos, CA (US);

Mark Damon Wheeler, Saratoga, CA (US);

Assignee:

NVIDIA CORPORATION, Santa Clara, CA (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G01S 17/89 (2020.01); H04N 19/17 (2014.01); G01C 21/30 (2006.01); G06T 9/00 (2006.01); G01S 17/86 (2020.01); G01S 17/90 (2020.01); G01S 17/931 (2020.01); G06V 20/58 (2022.01); G06V 20/56 (2022.01); G01C 21/00 (2006.01); G06T 9/20 (2006.01); G05D 1/02 (2020.01); G06F 16/174 (2019.01); B60R 11/04 (2006.01); G01C 11/02 (2006.01);
U.S. Cl.
CPC ...
G01S 17/89 (2013.01); G01C 21/30 (2013.01); G01C 21/3841 (2020.08); G01C 21/3848 (2020.08); G01C 21/3867 (2020.08); G01S 17/86 (2020.01); G01S 17/90 (2020.01); G01S 17/931 (2020.01); G06T 9/001 (2013.01); G06T 9/20 (2013.01); G06V 20/582 (2022.01); G06V 20/584 (2022.01); G06V 20/588 (2022.01); H04N 19/17 (2014.11); B60R 11/04 (2013.01); G01C 11/025 (2013.01); G05D 1/0274 (2013.01); G05D 2201/0213 (2013.01); G06F 16/1744 (2019.01);
Abstract

Embodiments relate to methods for efficiently encoding sensor data captured by an autonomous vehicle and building a high definition map using the encoded sensor data. The sensor data can be LiDAR data which is expressed as multiple image representations. Image representations that include important LiDAR data undergo a lossless compression while image representations that include LiDAR data that is more error-tolerant undergo a lossy compression. Therefore, the compressed sensor data can be transmitted to an online system for building a high definition map. When building a high definition map, entities, such as road signs and road lines, are constructed such that when encoded and compressed, the high definition map consumes less storage space. The positions of entities are expressed in relation to a reference centerline in the high definition map. Therefore, each position of an entity can be expressed in fewer numerical digits in comparison to conventional methods.


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