| The problem of mobile robot
localization by using sensor information appeals to different communities
since the need for accurate position has become crucial for many robot subtasks.
The Kalman filter (KF) has been acknowledged as an appropriate tool for
a suitable dynamic combination of the different measurements using the state
and measurement models. However, when there are discrete uncertainties about
the models, without additional restrictions, the performance of KF degrades
drastically as the predicted estimate tends to be updated by a wrong measurement.
This paper focuses on a suboptimal multiple model filter to deal with the
associated measurement/ model. Experimental and simulated results are achieved,
which prove the feasibility of the proposal. The experimental setup consists
of a structured environment constituted of elementary features such as walls
and corners, while a possibly unmodeled obstacle may be encountered. The
robot is equipped with odometry and a set of ultrasonic sensors. |
