ISPRS-Archives The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ISPRS-Archives Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci. 2194-9034 Copernicus Publications Göttingen, Germany 10.5194/isprs-archives-XLII-2-W13-895-2019 THE AUTOMATIC GENERATION OF AN ADAPTIVE NAVIGATION MODEL FOR INDOOR MAP MATCHING Wang P. 1 2 Shang J. 1 2 Zhou Z. 3 Wu Y. 1 2 Sun W. 4 School of Geography and Information Engineering, China University of Geosciences, Wuhan, China National Engineering Research Center for Geographic Information System, Wuhan, China GIScience Center, Department of Geography, University of Zurich, Switzerland Beijing Satellite Navigation Center, Beijing, China 05 06 2019 XLII-2/W13 895 902 Copyright: © 2019 P. Wang et al. 2019 This work is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of this licence, visit https://creativecommons.org/licenses/by/4.0/ This article is available from https://isprs-archives.copernicus.org/articles/XLII-2-W13/895/2019/isprs-archives-XLII-2-W13-895-2019.html The full text article is available as a PDF file from https://isprs-archives.copernicus.org/articles/XLII-2-W13/895/2019/isprs-archives-XLII-2-W13-895-2019.pdf

Indoor map matching has been an important technique to improve the indoor localization accuracy because it takes the advantage of available indoor building data and effectively decreases the localization cost. One of the spatial model involved in the indoor map matching, adaptive navigation model, could balance the accuracy and complexity of the model representation required by map matching by combining the medial axes and fine-grained grids according to the movement characteristics of pedestrians in open and narrow areas. In order to reduce the manual effort of producting a large number of models and update them, we propose an algorithm to automatically generate this model. Futermore, we use this algorithm to generate this model for three lab architectures, and the evaluation of the results of the ANM generated by the algorithm proves that the algorithm meets the requirements.