ISPRS-Archives ISPRS - 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-XLI-B4-275-2016 INDOOR NAVIGATION FROM POINT CLOUDS: 3D MODELLING AND OBSTACLE DETECTION Díaz-Vilariño L. 1 4 Boguslawski P. 2 Khoshelham K. 3 Lorenzo H. 4 Mahdjoubi L. 2 Dept. of Carthographic and Land Engineering, University of Salamanca, Hornos Caleros 50, 05003, Avila, Spain Faculty of Environment and Technology, Department of Architecture and the Built Environment, Frenchay Campus, Coldharbour Lane, University of the West of England, Bristol BS16 1QY, UK Department of Infrastructure Engineering, University of Melbourne, Melbourne, 3010, Australia Applied Geotechnologies Group, Dept. Natural Resources and Environmental Engineering, University of Vigo, Campus Lagoas-Marcosende, CP 36310 Vigo, Spain 13 06 2016 XLI-B4 275 281 This article is available from https://isprs-archives.copernicus.org/articles/isprs-archives-XLI-B4-275-2016.html The full text article is available as a PDF file from https://isprs-archives.copernicus.org/articles/isprs-archives-XLI-B4-275-2016.pdf

In the recent years, indoor modelling and navigation has become a research of interest because many stakeholders require navigation assistance in various application scenarios. The navigational assistance for blind or wheelchair people, building crisis management such as fire protection, augmented reality for gaming, tourism or training emergency assistance units are just some of the direct applications of indoor modelling and navigation. <br><br> Navigational information is traditionally extracted from 2D drawings or layouts. Real state of indoors, including opening position and geometry for both windows and doors, and the presence of obstacles is commonly ignored. <br><br> In this work, a real indoor-path planning methodology based on 3D point clouds is developed. The value and originality of the approach consist on considering point clouds not only for reconstructing semantically-rich 3D indoor models, but also for detecting potential obstacles in the route planning and using these for readapting the routes according to the real state of the indoor depictured by the laser scanner.