TUMindoor: An extensive image and point cloud dataset for visual indoor localization and mapping
Recent advances in the field of content-based image retrieval (CBIR) have made it possible to quickly search large image databases using photographs or video sequences as a query. With appropriately tagged images of places, this technique can be applied to the problem of visual location recognition. While this task has attracted large interest in the community, most existing approaches focus on outdoor environments only. This is mainly due to the fact that the generation of an indoor dataset is elaborate and complex. In order to allow researchers to advance their approaches towards the challenging field of CBIR-based indoor localization and to facilitate an objective comparison of different algorithms, we provide an extensive, high resolution indoor dataset. The free for use dataset includes realistic query sequences with ground truth as well as point cloud data, enabling a localization system to perform 6-DOF pose estimation.