Trajectory reconstruction for self-localization and map building

Abstract

We describe a method for the reconstruction of a driven trajectory of a mobile robot if the begin and end states of the trajectory are known, and intermediate readings from odometry are available. Our method uses a Kalman filter to combine a forward and backward dead-reckoning trajectory. We show that our method is more reliable for long trajectories than just combining the dead-reckoning trajectories independently.