Abstract

We examine the problem of learning a visual map of the environment
while maintaining an accurate pose estimate.  Our approach is based
on using two robots in a simple collaborative scheme; in practice, one 
of these robots can be much less capable than the other.
In many mapping contexts, a robot moves about collecting data (images, in
particular) which are later used to assemble a map; we can 
think of map construction as a training process.
Without outside information, as a robot collects training images, its
position estimate accumulates errors, thus corrupting its knowledge of
the positions from which observations are taken.  We address this
problem by deploying a second robot to observe the first one as it
explores, thereby establishing a \emph{virtual tether}, and enabling
an accurate estimate of the robot's position while it constructs the
map. We refer to this process as \emph{cooperative localization}. 
The images collected during this process are assembled into a
representation that allows vision-based position estimation from
a single image at a later date.
In addition to developing a formalism and concept, we validate our
results experimentally and present quantitative results demonstrating
the performance of the method in over 90 trials.