Methods, systems, and computer readable media for visual odometry using rigid structures identified by antipodal transform

• US 9,761,008 B2
• Filed: 03/07/2016
• Issued: 09/12/2017
• Est. Priority Date: 05/08/2014
• Status: Active Grant

• Current Assignee: Trustees Of The University Of Pennsylvania (University of Pennsylvania)
• Original Assignee: Trustees Of The University Of Pennsylvania (University of Pennsylvania)
• Inventors: Cleveland, Jonas, Daniilidis, Kostas
• Primary Examiner(s): Abdi, Amara
• Application Number:
  US15/063,495
• Publication Number:
  US 20160267678A1
• Time in Patent Office: 554 Days
• CPC Class Codes: G06T 2207/10016 Video; Image sequence
• CPC Class Codes: G06T 2207/20016 Hierarchical, coarse-to-fine, multiscale or multiresolution image processing; Pyramid transform
• CPC Class Codes: G06T 2207/30244 Camera pose
• CPC Class Codes: G06T 7/246 using feature-based methods, e.g. the tracking of corners or segments
• CPC Class Codes: G06T 7/73 using feature-based methods

Abstract

The subject matter described herein includes methods for visual odometry using rigid structures identified by an antipodal transform. One exemplary method includes receiving a sequence of images captured by a camera. The method further includes identifying rigid structures in the images using an antipodal transform. The method further includes identifying correspondence between rigid structures in different image frames. The method further includes estimating motion of the camera based on motion of corresponding rigid structures among the different image frames.

Claims

1. A method for hybrid motion model computation from matching feature points, the method comprising;

identify feature points in a current image frame and at least two previous image frames captured by a camera;

for a triplet of image frames including the current image frame and a previous two image frames, estimate inter-frame camera position at each frame based on matching of the feature points across the frames in the triplet; and

for the triplet of image frames, using line correspondences in the frames to compute a line-based trifocal tensor and an estimation of inter-frame camera position at every frame, wherein the line-based trifocal tensor is based on a line whose inter-frame rotation and translation are tracked and used to determine inter-frame rotation and translation of the camera for the triplet of image frames.