Comparison of 3D feature registration techniques for indoor mapping

Maps are used extensively to understand the surrounding environment and to navigate through it. This has motivated research in localization and mapping and as a result numerous algorithms have been proposed to construct different types of maps. The mapping problem involves many difficulties such as: the estimation of the sensor position and orientation at each observation, the correct interpretation of data and the error minimization in aligning observations. In this paper a comprehensive overview of the visual SLAM problem is provided along with a comparison of different algorithms used in the construction of 3D maps. The algorithms have been tested on standard 3D datasets of indoor environments.