Abstract

We consider the problem of map learning while maintaining ground-truth
pose estimates. Map learning is important in tasks that require a model
of the environment or some of its features. As a robot collects data,
uncertainty about its position accumulates and corrupts its knowledge
of the positions from which observations are taken. We address this
problem by employing <i>cooperative
localization</i>; that is, deploying a second robot to observe the other
as it explores, thereby establishing a <i>virtual tether</i>, and enabling
an accurate estimate of the robot's position while it constructs the map.
This paper presents our approach to this problem in the context of learning
a set of visual landmarks useful for pose estimation. In addition to developing
a formalism and concept, we validate our results experimentally and present
quantitative results demonstrating the performance of the method.