US 11,780,465 B2
System and method for free space estimation
Raajitha Gummadi, Manchester, NH (US); Christopher C. Langenfeld, Nashua, NH (US); Michael J. Slate, Merrimack, NH (US); and Christopher J. Principe, Nahant, MA (US)
Assigned to DEKA Products Limited Partnership, Manchester, NH (US)
Filed by DEKA Products Limited Partnership, Manchester, NH (US)
Filed on Sep. 23, 2022, as Appl. No. 17/951,331.
Application 17/951,331 is a continuation of application No. 16/925,855, filed on Jul. 10, 2020, granted, now 11,455,806.
Claims priority of provisional application 62/872,583, filed on Jul. 10, 2019.
Prior Publication US 2023/0016896 A1, Jan. 19, 2023
This patent is subject to a terminal disclaimer.
Int. Cl. G06K 9/00 (2022.01); B60W 60/00 (2020.01); G06T 7/33 (2017.01); G05D 1/00 (2006.01); G05D 1/02 (2020.01); G06V 20/58 (2022.01); G06V 20/56 (2022.01); G06F 18/2415 (2023.01); G06V 10/77 (2022.01); G01S 17/894 (2020.01)
CPC B60W 60/0011 (2020.02) [G05D 1/0088 (2013.01); G05D 1/0251 (2013.01); G06F 18/2415 (2023.01); G06T 7/33 (2017.01); G06V 10/7715 (2022.01); G06V 20/58 (2022.01); G06V 20/588 (2022.01); G01S 17/894 (2020.01); G05D 2201/0213 (2013.01); G06T 2207/10028 (2013.01); G06T 2207/30256 (2013.01); G06T 2207/30261 (2013.01)] 25 Claims
OG exemplary drawing
 
1. A method for estimating free space based on image data and point cloud data, the free space used for navigating an autonomous vehicle, the method comprising:
semantically classifying the image data based on a machine learning model forming point classifications and point classification probabilities;
associating each point in the point cloud data to the image data that are spatially co-located with the point cloud data;
classifying each of the points as an obstructed space or a non-obstructed space based on the spatial association of each of the points with the semantically classified image data forming obstructed points and non-obstructed points;
forming a grid of the obstructed points and non-obstructed points within a pre-selected area surrounding the autonomous vehicle; and
estimating the free space in the pre-selected area by associating the obstructed points with a first probability based at least on (1) noise in the point cloud data, (2) a second probability that the point cloud data are reliable, (3) a distance from the non-obstructed points to the obstructed space closest to the non-obstructed points, (4) a third probability that the point classifications are correct, and (5) presence of the non-obstructed space.