Simultaneous localization and mapping survey based on filtering techniques

Simultaneous Localization and Mapping (SLAM) problem has been an active area of research in robotics for more than two decades. This paper reviews SLAM based on different filtering techniques used to do the state estimation of the mobile robot. The filtering techniques included in this study are Kalman filter, particle filter, H infinity filter. It can be concluded that each filtering technique has its own advantages and disadvantages as it is very dependent on the situations. Kalman filter is much suitable for dealing with Gaussian distribution. Particle filter is selected for large-scale environment as its computation complexity is logarithmic compared to Kalman filter which has quadratic complexity. H infinity filter is used to improve the convergence of SLAM system.