Scale-Space Features in 1D Omnidirectional Images
Amy J. Briggs
Carrick Detweiler
Peter Mullen
Daniel Scharstein
Abstract
We define a family of interest operators for extracting features from
one-dimensional omnidirectional images, and explore the utility of
such features for navigation and localization of a mobile robot
equipped with an omnidirectional camera. A 1D circular image, formed
by averaging the scanlines of a cylindrical panorama, provides a
compact representation of the robot's surroundings. Feature detection
proceeds by applying local interest operators in the scale space of
the image. The work is inspired by the recent success of similar
operators developed for 2D images. The advantages of using features
in omnidirectional 1D images are fast processing times and low storage
requirements, which allows a dense sampling of views. We present
experimental results on real images that demonstrate that our features
are insensitive to noise, illumination variations, and changes in
camera orientation. We also demonstrate that most features remain
stable over changes in viewpoint and in the presence of some
occlusion, thus allowing reliable tracking of features through
sequences of frames.
A pdf version of this paper
is available.
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