The use of sensors for the mobile robots is an essential part in the context of autonomous navigation. The data processing from various sensors individually contribute to the environment perception for the mobile robot. Once the heterogeneous data is available, the information gain can be further more extended by fusing the data from different sensors. This can be achieved with various estimation techniques, like Kalman filtering. The fused data is especially useful for the position estimation, localization or mapping. This book covers both the theoretical and the practical details of the perception, localization and mapping based on sensor fusion for autonomous mobile robots.