SLAM for mobile robots using laser range finder and monocular vision

Localization and map building are two essential tasks for an autonomous mobile robot's indoor navigation without a prior map. This paper describes a mobile robot system designed for simultaneous localization and mapping (SLAM) for an autonomous mobile robot in an indoor environment. Due to variant sensor modeling for laser range finder and CCD camera, weighted least square fitting and Canny operator are used to extract certain two-dimensional environmental features and vertical edges respectively. Using Kalman filtering (KF) to localization and grid map building simultaneously are also presented. When implemented on a Zixing mobile robot produced by Harbin Institute of Technology (Weihai), the localization technique correctly localized the robot while exploring and mapping.

[1]  Roland Siegwart,et al.  Multisensor on-the-fly localization: : Precision and reliability for applications , 2001, Robotics Auton. Syst..

[2]  Randall Smith,et al.  Estimating Uncertain Spatial Relationships in Robotics , 1987, Autonomous Robot Vehicles.

[3]  Nobuyuki Kita,et al.  3D simultaneous localisation and map-building using active vision for a robot moving on undulating terrain , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[4]  Darius Burschka,et al.  Advances in Computational Stereo , 2003, IEEE Trans. Pattern Anal. Mach. Intell..

[5]  John J. Leonard,et al.  Directed Sonar Sensing for Mobile Robot Navigation , 1992 .

[6]  Sebastian Thrun,et al.  Simultaneous localization and mapping with active stereo vision , 2004, 2004 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (IEEE Cat. No.04CH37566).

[7]  James L. Crowley,et al.  Position estimation for a mobile robot using vision and odometry , 1992, Proceedings 1992 IEEE International Conference on Robotics and Automation.

[8]  Henrik I. Christensen,et al.  Pose tracking using laser scanning and minimalistic environmental models , 2001, IEEE Trans. Robotics Autom..

[9]  Roland Siegwart,et al.  A hybrid approach for robust and precise mobile robot navigation with compact environment modeling , 2001, Proceedings 2001 ICRA. IEEE International Conference on Robotics and Automation (Cat. No.01CH37164).

[10]  Patric Jensfelt,et al.  Active global localization for a mobile robot using multiple hypothesis tracking , 2001, IEEE Trans. Robotics Autom..

[11]  José A. Castellanos,et al.  Multisensor fusion for simultaneous localization and map building , 2001, IEEE Trans. Robotics Autom..