This thesis describes and investigates different approaches to indoor mapping and navigation. A system capable of mapping large indoor areas with a stereo camera and/or a laser camera mounted to e.g. a robot or a human is developed. The approaches investigated in this report are either based on Simultaneous Lo- calisation and Mapping (SLAM) techniques, e.g. Extended Kalman Filter-SLAM (EKF-SLAM) and Smoothing and Mapping (SAM), or registration techniques, e.g. Iterated Closest Point (ICP) and Normal Distributions Transform (NDT).In SLAM, it is demonstrated that the laser camera can contribute to the stereo camera by providing accurate distance estimates. By combining these sensors in EKF-SLAM, it is possible to obtain more accurate maps and trajectories compared to if the stereo camera is used alone.It is also demonstrated that by dividing the environment into smaller ones, i.e. submaps, it is possible to build large maps in close to linear time. A new approach to SLAM based on EKF-SLAM and SAM, called Submap Joining Smoothing and Mapping (SJSAM), is implemented to demonstrate this.NDT is also implemented and the objective is to register two point clouds from the laser camera to each other so that the relative motion can be obtained. The NDT implementation is compared to ICP and the results show that NDT performs better at estimating the angular difference between the point clouds.