A fully autonomous indoor mobile robot using SLAM

Abstract

This paper presents a complete Simultaneous Localization and Mapping (SLAM) solution for indoor mobile robots, addressing feature extraction, autonomous exploration and navigation using the continuously updating map. The platform used is Pioneer PeopleBot equipped with SICK Laser Measurment System (LMS) and odometery. Our algorithm uses Hough Transform to extract the major representative features of indoor environment such as lines and edges. Localization is accomplished using Relative Filter which depends directly on the perception model for the correction of error in the robot state. Our map for localization is in the form of a landmark network whereas for navigation we are using occupancy grid. The resulting algorithm makes the approach computationally lightweight and easy to implement. Finally, we present the results of testing the algorithm in Player/Stage as well as on PeopleBot in our Robotics and Control Lab.