In order to
perform useful tasks the mobile robot's current pose must be accurately known.
Problem of finding and tracking the mobile robot's pose is called localization,
and can be global or local. In this paper we address the problem of mobile
robot's local localization or pose tracking with prerequisites of known
starting pose, robot kinematics and world model. Pose tracking is mostly based
on odometry, which has the problem of accumulating errors in an unbounded
fashion. To overcome this problem sensor fusion is commonly used. This paper
describes a simple odometry calibration method and compares two fusion methods
of calibrated odometry data and sonars' range data based on Kalman Filter
theory. One fusion method is based on standard Extended Kalman Filter and
another one, proposed in this paper, on the Unscented Kalman Filter. Occupancy
grid map is used as the world model, which is beneficial because only sonars'
range measurement uncertainty has to be considered. If a feature based map is used,
as the world model then an additional uncertainty regarding the feature/range
reading assignment must be also considered. Experimental results obtained with
the Pioneer 2DX mobile robot (manufacturer ActivMedia Robotics) show that
better accuracy of pose estimation and smoother robot motion can be obtained
with Unscented Kalman Filter.
Keywords: Non-linear Kalman Filter, Mobile
Robot, Pose Tracking, Occupancy Grid Map
Non-linear Kalman filter based relative localization
most wheeled mobile robot have an odometric system implemented in their
controller itís used for mobile robot pose estimation without additional range
sensors. Using additional range sensors measurements and a world model, pose
correction can be computed using various sensor fusion techniques like the
non-linear Kalman filter.
Non-linear Kalman filter localization block
Position estimation results for EKF based
Position estimation results for UKF based
Standard deviation during the experiments
For more details see our paper "Sonar-based Pose Tracking of Indoor Mobile Robots" (