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[Advanced Control Systems Group] [Autonomous Mobile Robotics Group]





Mobile Robots Localization and Map Building


Sonar based localization

Sonar Based Localization


Introduction

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

Although 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 scheme

 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 


Position estimation results for EKF based relative localization

Position estimation results for UKF based relative localization

Standard deviation during the experiments

 

For more details see our paper "Sonar-based Pose Tracking of Indoor Mobile Robots" ( PDF ).

 

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