Robot Steering With Spectral Image Information
IEEE Transactions on Robotics, 21 (2):
247-251 (April 2005)Abstract
We introduce a method for rapidly classifying visual scenes, globally
along a small number of navigationally relevant dimensions: depth
of scene, presence of obstacles, path vs. non-path, and orientation
of path. We show that the algorithm reliably classifies scenes in
terms of these high-level features, based on global or coarsely localized
spectral analysis analogous to early-stage biological vision. We
use this analysis to implement a real-time visual navigational system
on a mobile robot, trained online by a human operator. We demonstrate
successful training and subsequent autonomous path following for
two different out-door environments, a running track and a concrete
trail. Our success with this technique suggests a general applicability
to autonomous robot navigation in a variety of environments.
Description
Dissertation
