Comparative Study of Two Localization Approaches for Mobile Robots in an Indoor Environment

Abstract

In the last years, mobile robot localization has been developed significantly due to the need for accurate solutions to determine the position and orientation of the wheeled mobile robot (WMR) in a given environment. Many different sensors have been used to solve the problem. For instance, ultrasonic sensors, laser, or infrared sensors are also used to determine the pose of the WMR. However, sensors are sensitive to noise measurements and disturbances, which can distort the acquired information. For this reason, adequate algorithms should be used to reduce these uncertainties and determine the optimal pose of the WMR. In this research work, we focus on the comparative study of the most used algorithms, using landmarks as sensors, which are the extended Kalman filter and particle filter. Further, for an effective comparison, the simulation results were conducted and analyzed using different performance criteria. The simulations results showed better estimation performance achieved by the particle filter being compared to the extended Kalman filter when the sensors are subject to non-Gaussian noises.