CPC G01C 21/3848 (2020.08) [G01C 21/3811 (2020.08); G06F 16/29 (2019.01); G06F 16/587 (2019.01); G06T 7/11 (2017.01); G06T 7/74 (2017.01); G06V 20/582 (2022.01); G06V 20/588 (2022.01); G06T 2207/10016 (2013.01); G06T 2207/30244 (2013.01); G06T 2207/30256 (2013.01)] | 16 Claims |
1. A method, comprising:
obtaining, from at least one camera associated with a vehicle traveling on a road, a sequence of images of an environment of the road, each image being associated with a location where that image was obtained;
generating a local map representation of an area of the road using at least some images from the sequence of images and the locations associated therewith, the generating including:
processing the at least some of the images to detect a landmark object representing a landmark in the environment of the road, the processing including, for each image of the at least some of the images:
allocating at least one object class associated with landmark objects to a region in that image that includes the landmark object based on a result of a segmentation performed using a specified machine learning algorithm;
identifying the region as a region of interest based on the at least one object class allocated thereto; and
processing the region of interest to generate a bounding area in which the landmark object is included;
determining at least one transformation for mapping the landmark object between the at least some of the images; and
based on the at least one transformation and the locations associated with the at least some of the images, generating a three-dimensional representation for the landmark object relative to the area of the road;
comparing the local map representation with some or all of a reference map to identify a corresponding section of the reference map; and
selectively updating the corresponding section of the reference map based on the local map representation.
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