The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Publications Copernicus
Articles | Volume XLII-2/W13
05 Jun 2019
 | 05 Jun 2019


M. Li and F. Rottensteiner

Keywords: Indoor localization, Image retrieval, Geometric constraint, Bag-of-visual-word, SLAM

Abstract. With an increasing interest in indoor location based services, vision-based indoor localization techniques have attracted many attentions from both academia and industry. Inspired by the development of simultaneous localization and mapping technique (SLAM), we present a visual SLAM-based approach to achieve a 6 degrees of freedom (DoF) pose in indoor environment. Firstly, the indoor scene is explored by a keyframe-based global mapping technique, which generates a database from a sequence of images covering the entire scene. After the exploration, a feature vocabulary tree is trained for accelerating feature matching in the image retrieval phase, and the spatial structures obtained from the keyframes are stored. Instead of querying by a single image, a short sequence of images in the query site are used to extract both features and their relative poses, which is a local visual SLAM procedure. The relative poses of query images provide a pose graph-based geometric constraint which is used to assess the validity of image retrieval results. The final positioning result is obtained by selecting the pose of the first correct corresponding image.