This thesis explores the problem of Simultaneous Localization and Mapping (SLAM) with a focus on combining different sensors to build a more robust, accurate, and reliable localization framework. Localization without prior knowledge of the environment is considered to be one of the most challenging problems in the field of robotics. The core contribution of this thesis is the development of a high-level sensor fusion solution, which enables simple integration of unconventional sensors not typically used for robot localization. A capacitive sensor for sensing floor joists directly under the robot is proposed as an example of an unconventional sensor. A neural network is implemented to fuse multiple measurements and build a model of likely joist locations in unexplored regions. Two different sensor fusion approaches are proposed. The first solution explores robot localization with a-priori map knowledge. Prior map knowledge removes the requirement for map learning and focuses the problem on fusion of the different sensor maps. With this focus a high-level scalable sensor fusion architecture is implemented. Results show an improvement when using this algorithm to incorporate new sensors into the robot localization configuration. The approach also resolves the problem where the map is known but the starting location is not. The second fusion approach proposes a complete multi-sensor SLAM algorithm without a-priori map knowledge. The capacitive sensor is incorporated into the algorithm to demonstrate the scalability of the approach. After incorporating multiple sensors information into the solution, the peak error and average error of the estimated robot position are both reduced; while simultaneously enabling greater robustness through redundant sensors.