Simultaneous localization and mapping (SLAM) has been developed in many years with lot of improvements in both of hardware and software. The time update, the measurement update and the Kalman Filter of SLAM is generally introduced in this thesis. To overcome the localization problem in mobile robot, the two wheeled differential vehicle model is used as the time update. In particularly, a laser range finder is used to scan the environment and then the landmarks are extracted by Kanade–Lucas–Tomasi (KLT) algorithm. Finally, the Unscented Kalman Filter is used to estimate robot position based on the time update and the measurement data.
The experimental results proved that using image processing – KLT algorithm - to process the laser range data is a possible implementation. Hence, the image-processing based KLT algorithm has great potential in future implementations. Moreover, some experiments with Amigobot has shown that, under the effects of system noise, vehicle model and measurement data, SLAM algorithm is well correspondence. The Matlab SLAM package of Tim Bailey is a good example for a simple and visual program to see how SLAM is running on mobile robot.

Simultaneous localization and mapping (SLAM) has been developed in many years with lot of improvements in both of hardware and software. The time update, the measurement update and the Kalman Filter of SLAM is generally introduced in this thesis. To overcome the localization problem in mobile robot, the two wheeled differential vehicle model is used as the time update. In particularly, a laser range finder is used to scan the environment and then the landmarks are extracted by Kanade–Lucas–Tomasi (KLT) algorithm. Finally, the Unscented Kalman Filter is used to estimate robot position based on the time update and the measurement data.
The experimental results proved that using image processing – KLT algorithm - to process the laser range data is a possible implementation. Hence, the image-processing based KLT algorithm has great potential in future implementations. Moreover, some experiments with Amigobot has shown that, under the effects of system noise, vehicle model and measurement data, SLAM algorithm is well correspondence. The Matlab SLAM package of Tim Bailey is a good example for a simple and visual program to see how SLAM is running on mobile robot.

[1] P. N. M.W.M.G Dissanayake, H.F. Durrant-Whyte, S.Clark, and M. Csobra, "An experimental and theoretical investigation into simultaneous localisation and map builing (slam)," Proc.6th International Symposium on Experimental Robotics, Sydney, Australia, p. 10, 1999.
[2] M. W. M. G. Dissanayake, et al., "A solution to the simultaneous localization and map building (SLAM) problem," Robotics and Automation, IEEE Transactions on, vol. 17, pp. 229-241, 2001.
[3] J. L. A. Jones, MA, US), Mack, Newton E. (Somerville, MA, US), Nugent, David M. (Newport, RI, US), Sandin, Paul E. (Randolph, MA, US). (2005, Autonomous floor-cleaning robot. (US patent No.6883201). Available: http://www.freepatentsonline.com/6883201.html
[4] http://en.wikipedia.org/wiki/Kalman_filter.
[5] S. K. A. T. Pieniężny, "A comparison of estimation accuracy by the use of KF, EKF & UKF filters ", WIT Transactions on Modelling and Simulation, vol. 46, p. 10, 2007.
[6] A. M. Viet Nguyen, Nicola Tomatis, Roland Siegwart, "A Comparison of Line Extraction Algorithms using 2D Laser Rangefinder for Indoor Mobile Robotics," Conference on Intelligent Robots and Systems, IROS’2005, p. 6, 2005.
[7] T. K. Bruce D. Lucas, "An Iterative Image Registration Technique with an Application to Stereo Vision ", International Joint Conference on Artificial Intelligence, pages 674–679, 1981.
[8] T. K. Carlo Tomasi, "Detection and Tracking of Point Features," Carnegie Mellon University Technical Report CMU-CS-91-132, April 1991.
[9] A. R. D. S. a. M. Client, "Team AmigoBot™ Operations Manual ", ARIA Robotics Development Software and MobileEyes Client.