Reliable Mobile Robot Navigation from Unreliable Visual Cues
Amy J. Briggs
Daniel Scharstein
Stephen Abbott
Abstract
Vision-based mobile robot navigation requires robust methods for
planning and executing tasks due to the unreliability of visual
information. In this paper we propose a new method for reliable
vision-based navigation in an unmodeled dynamic environment.
Artificial landmarks are used as visual cues for navigation. Our
system builds a visibility graph among landmark locations during an
exploration phase and then uses that graph for navigation. To
deal with temporary occlusion of landmarks, long-term
changes in the environment, and inherent uncertainties in the
landmark detection process, we use a probabilistic model of landmark
visibility. Based on the history of previous observations made,
each visibility edge in the graph is annotated with an estimated
probability of landmark detection. To solve a navigation task, our
algorithm computes the \emph{expected} shortest paths between all
landmarks and the specified goal, by solving
a special instance of a Markov decision process. The paper presents
both the probabilistic expected shortest path planner and the
landmark design and detection algorithm, which finds landmark
patterns under general affine transformations in real-time.
A gzip'ed postscript version of this paper
is available.
Back to Amy Briggs' home page