NDLI: New Visual Invariants for Terrain Navigation Without 3D Reconstruction

Content Provider	Springer Nature Link
Author	Young, Gin Shu Herman, Martin Hong, Tsai Hong Jiang, David Yang, Jackson C.S.
Copyright Year	1998
Abstract	For autonomous vehicles to achieve terrain navigation, obstacles must be discriminated from terrain before any path planning and obstacle avoidance activity is undertaken. In this paper, a novel approach to obstacle detection has been developed. The method finds obstacles in the 2D image space, as opposed to 3D reconstructed space, using optical flow. Our method assumes that both nonobstacle terrain regions, as well as regions with obstacles, will be visible in the imagery. Therefore, our goal is to discriminate between terrain regions with obstacles and terrain regions without obstacles. Our method uses new visual linear invariants based on optical flow. Employing the linear invariance property, obstacles can be directly detected by using reference flow lines obtained from measured optical flow. The main features of this approach are: (1) 2D visual information (i.e., optical flow) is directly used to detect obstacles; no range, 3D motion, or 3D scene geometry is recovered; (2) knowledge about the camera-to-ground coordinate transformation is not required; (3) knowledge about vehicle (or camera) motion is not required; (4) the method is valid for the vehicle (or camera) undergoing general six-degree-of-freedom motion; (5) the error sources involved are reduced to a minimum, because the only information required is one component of optical flow. Numerous experiments using both synthetic and real image data are presented. Our methods are demonstrated in both ground and air vehicle scenarios.
Starting Page	45
Ending Page	71
Page Count	27
File Format	PDF
ISSN	09205691
Journal	International Journal of Computer Vision
Volume Number	28
Issue Number	1
e-ISSN	15731405
Language	English
Publisher	Kluwer Academic Publishers
Publisher Date	1998-01-01
Publisher Place	Boston
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Artificial Intelligence (incl. Robotics) Computer Imaging, Graphics and Computer Vision Image Processing Automation and Robotics
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence Computer Vision and Pattern Recognition Software

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