Ford Global Technolgoies, LLC

Devarth Parikh

Nalin Bendapudi

Gaurav Pandey

Ankit Girish Vora

Siddharth Agarwal

Matthew Baer Wilmes

Shenbagaraj Kannapiran

A computer that includes a processor and a memory, the memory including instructions executable by the processor to select first side stereo images of first and second pairs of stereo images acquired at first and second time steps, respectively, and mask first and second first side stereo images, determine point features in masked first and second first side stereo images and determine line features in masked first and second first side stereo images. Matching point features in the masked first and second first side stereo images can be determined using a first attention/graph neural network (attn/GNN) based on keypoints determined based on the line features. Matching line features in the masked first and second first side stereo images can be determined using a second attn/GNN based on keypoints determined based on the line features. Three-dimensional (3D) locations in a scene can be determined by determining stereo disparity based on the matched point features included in the first first side stereo image and point features determined in a first second side stereo image of first and second stereo pairs of images and a 3D stereo camera pose can be determined by determining a perspective-n-point and line algorithm on the 3D locations.

Visual odometry for operating a movable device

A computer includes a processor and a memory, and the memory stores instructions executable by the processor to receive a first top-view image of an area obtained from a sensor external to a vehicle, generate a second top-view image of the area based on data from a camera of the vehicle, and determine an estimated location of the vehicle in the area based on comparing the first top-view image and the second top-view image.

Localization of a vehicle

First feature points can be determined which correspond to pose-invariant surface model properties based on first data points included in a first lidar point cloud acquired by a sensor. A three-dimensional occupancy grid can be determined based on first data points included in the first lidar point cloud. Dynamic objects in a second lidar point cloud acquired by the sensor can be determined based on the occupancy grid. Second feature points can be determined which correspond to pose-invariant surface model properties based on second data points included in the second lidar point cloud not including the dynamic objects. A difference can be determined between corresponding feature points included in the first feature points and the second feature points. A traffic infrastructure system can be alerted based on the difference exceeding a threshold.

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Ann Arbor, MI, United States of America

Devarth Parikh