Simultaneous Localization and Mapping (SLAM), is the most important task in making a mobile robots fully autonomous. So far three general algorithms proposed to solve SLAM problem. The first proposed solution to the SLAM was founded based on Kalman Filter and eventually Extended Kalman Filter (EKF-SLAM). To deal with deficiencies in EKF-SLAM, Fast-SLAM was introduced as the second solution to SLAM problems. Fast-SLAM uses particle filter instead of Kalman filter. iSAM is the third solution proposed as the solution to SLAM. In this thesis, a grid based 2D Fast-SLAM algorithm was implemented in two wheeled laboratory mobile robot, fabricated in Dynamic and Robotic research center, to develop an online SLAM platform for the robot. Robot Operating System (ROS) platform was used to program the algorithm. ROS is an open source operating system recently developed for software implementation in robotic system. The fabricated mobile robot is equipped with a Laser Range Finder (LRF) sensor for environment scanning, two encoders attached to the wheels for wheel Odometery and an Inertia Measurement Unit (IMU) to measure the robot attitude motion. Initially the online Fast-SLAM algorithm was developed for uneven planar motion of the robot. Moving on an uneven surface like a slope leaded to error in robot localization and consequently error in mapping. Using the IMU data, in this thesis, kinematic equations were adjusted for robot motion on slopes. The adjusted algorithm tested on a slope made for this test by wood. The test results showed a significant improvement in the algorithm accuracy Keywords Fast-SLAM, IMU, Slope, special odometry
