Simultaneous localization and mapping (SLAM) is a process in which a mobile robot travels through an environment and concurrently makes a momentary map of the environment and uses that map to localize itself. The simultaneous localization and mapping is currently one of the most challenging problems in the field of autonomous mobile robots and providing a solution to SLAM may open doors to the world of truly autonomous robots. The most significant contribution of this dissertation is to provide a novel approach to Simultaneous Localization and Mapping problem in extensive outdoor environments
and based on estimation approach. The new approach is called Unscented HybridSLAM filter which presents a consistent mathematical model out of a rigorous probabilistic Bayesian-based framework. It is theoretically proven that the map converges and how the new approach can handle correlations that arise between error in motion and error in observation. It is also shown that there is no need for a large storage of information since the inherent structure of Unscented HybridSLAM does not require memory as much as its counterpart filters. The map evolution of the new algorithm is examined in
detail as well as its performance. The new approach is compared to currently used algorithms in particular EKF-SLAM, FastSLAM, and HybridSLAM and results are probed and discussed in different simulated scenarios. Together, the theoretical modeling and simulations results prove the consistency of Unscented HybridSLAM and show that it is possible to apply Unscented HybridSLAM as an alternative algorithm for real implementations.