A comparison of simultaneous localization and mapping (SLAM) methods and an implementation of EKF SLAM on ATRV-Mini platform - Chen, Lalaine Michelle A.
The Simultaneous Localization and Mapping (SLAM) problem is a prominent area of research in the mobile robotics community. For the past few years, there have been considerable advancements in this area. Although researchers have developed efficient algorithms to solve the problem, there are only a few quantitative studies that focus on the comparison of the different approaches used. The analysis of the usage of computing resources and performance helps in determining the benefits and pitfalls of an algorithm. For this paper, the proponents present a comparison of two approaches to help solve the SLAM problem: the Estimation-Theoretic or Extended Kalman Filter (EKF) approach and the Particle Filter or FastSlam approach. The different approaches were implemented using Microsoft Robotics Studion with both simulated and real world data. The researchers were also able to implement Extended Kalman Filter (EKF) SLAM for an ATRV miniplatform equipped with ultrasonic sensors and a compass. It is controlled through a Linksys WRT54GL firmware that runs on OpenWrt. A feature-based mapping approach was used in order to overcome hardware limitations. Results show that the WRT54GL was able to successfully process and execute EKF SLAM despite its limited memory and processing power.