Real-Time 2D Mapping and Localization Algorithms for Mobile Robot Applications

Abstract

The purpose of this paper is to show an approach in 2D localization and real-time mapping for robot applications that combine the Particle Filter algorithm, Extended Kalman Filter (EKF), and Iterative Closest Point (ICP). The closing loop method is added and shows satisfactory 2D mapping and localization results. We tested our approach to large floor buildings. For testing, we used a two-wheeled differential drive robot equipped with an optical encoder, laser scanner and gyroscope. Test results show that an accurate map of large high-rise buildings can be produced. Real-time mapping can reach a resolution of 5 cm. Automatic localization of cellular robots in unknown environments is one of the most fundamental problems in robot navigation. This is a complex problem due to the stringent requirements on cellular robots, especially those relations to accuracy, durability, and computational efficiency. The conclusions from this study can help in developing real-time 2D mapping for robot applications that process 2D cloud points directly.