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:
Jun. 06, 2023

Filed:

Feb. 04, 2020
Applicant:

Nio Technology (Anhui) Co., Ltd., Anhui, CN;

Inventors:

Arun C S Kumar, Milpitas, CA (US);

Disha Ahuja, San Jose, CA (US);

Ashwath Aithal, Fremont, CA (US);

Assignee:
Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06T 15/00 (2011.01); G01C 21/26 (2006.01); G06T 17/00 (2006.01); G01S 17/89 (2020.01); G06T 11/20 (2006.01); G06T 7/10 (2017.01); G06T 7/11 (2017.01); G01S 17/931 (2020.01); G01S 7/48 (2006.01); G01S 17/42 (2006.01); G06T 3/40 (2006.01); G06F 18/24 (2023.01);
U.S. Cl.
CPC ...
G01C 21/26 (2013.01); G01S 7/4802 (2013.01); G01S 17/42 (2013.01); G01S 17/89 (2013.01); G01S 17/931 (2020.01); G06F 18/24 (2023.01); G06T 3/40 (2013.01); G06T 7/10 (2017.01); G06T 7/11 (2017.01); G06T 11/20 (2013.01); G06T 17/00 (2013.01); G06T 2207/10028 (2013.01); G06T 2207/30252 (2013.01); G06T 2210/12 (2013.01);
Abstract

Embodiments include a method for ground surface segmentation on sparse Light Detection And Ranging (LiDAR) point clouds comprising: reading a LiDAR point cloud from a LiDAR sensor, the LiDAR point cloud comprising data representing one or more objects in physical surroundings detected by the LiDAR sensor; voxelizing the LiDAR point cloud to produce a three-dimensional representation of each of the one or more objects; constructing a maximum height map from the three-dimensional representation of each of the one or more objects, the maximum height map comprising a two-dimensional mapping of spatial points representing each of the one or more objects; performing minimum filtering on the spatial points of the maximum height map; and classifying each spatial point as a ground point or a non-ground point based on the minimum filtering of each spatial point.


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