Global Localization using Distinctive Visual Features
We have previously developed a mobile robot system which uses scale invariant
visual landmarks to localize and simultaneously
build a 3D map of the environment
In this paper, we look at global localization, also known
as the kidnapped robot problem,
where the robot localizes itself globally, without any prior location estimate.
This is achieved by matching distinctive landmarks in the current frame to
a database map.
A Hough Transform approach and a RANSAC approach for global localization
are compared, showing that RANSAC is much more efficient. Moreover, robust
global localization can be achieved by matching a
small sub-map of the local region built from multiple frames.