Pedestrian mobile mapping system for indoor environments based on MEMS IMU and range camera

• Autorzy: Haala N. , Fritsch D. , Peter M. , Khosravani A.
• Języki publikacji: EN

Abstrakty

EN

This paper describes an approach for the modeling of building interiors based on a mobile device, which integrates modules for pedestrian navigation and low-cost 3D data collection. Personal navigation is realized by a foot mounted low cost MEMS IMU, while 3D data capture for subsequent indoor modeling uses a low cost range camera, which was originally developed for gaming applications. Both steps, navigation and modeling, are supported by additional information as provided from the automatic interpretation of evacuation plans. Such emergency plans are compulsory for public buildings in a number of countries. They consist of an approximate floor plan, the current position and escape routes. Additionally, semantic information like stairs, elevators or the floor number is available. After the user has captured an image of such a floor plan, this information is made explicit again by an automatic raster-to-vector-conversion. The resulting coarse indoor model then provides constraints at stairs or building walls, which restrict the potential movement of the user. This information is then used to support pedestrian navigation by eliminating drift effects of the used low-cost sensor system. The approximate indoor building model additionally provides a priori information during subsequent indoor modeling. Within this process, the low cost range camera Kinect is used for the collection of multiple 3D point clouds, which are aligned by a suitable matching step and then further analyzed to refine the coarse building model.

Słowa kluczowe

EN

urban; reconstruction; IMU; navigation; building; modeling; architecture

PL

IMU; nawigacja; rekonstrukcja; budynek; modelowanie; architektura

• Wydawca: Zarząd Główny Stowarzyszenia Geodetów Polskich
• Czasopismo: Archiwum Fotogrametrii, Kartografii i Teledetekcji
• Rocznik: 2011
• Tom: Vol. 22
• Strony: 159--172
• Opis fizyczny: Bibliogr. 16 poz.

Twórcy

autor

Haala N.

• Institute for Photogrammetry (ifp), University of Stuttgart, Geschwister-Scholl-Str. 24D, D-70174 Stuttgart, Germany

autor

Fritsch D.

• Institute for Photogrammetry (ifp), University of Stuttgart, Geschwister-Scholl-Str. 24D, D-70174 Stuttgart, Germany

autor

Peter M.

• Institute for Photogrammetry (ifp), University of Stuttgart, Geschwister-Scholl-Str. 24D, D-70174 Stuttgart, Germany

autor

Khosravani A.

• Institute for Photogrammetry (ifp), University of Stuttgart, Geschwister-Scholl-Str. 24D, D-70174 Stuttgart, Germany

Bibliografia

• 1. Bauer, W. & Mohl, H.-U., 2005. Das 3D-Stadtmodell der Landeshauptstadt Stuttgart. In: 3D-Geoinformationssysteme: Grundlagen und Anwendungen, eds. Coors, V. and Zipf, A. e., Wichmann Verlag, pp. 265-278.
• 2. Becker, S. & Haala, N., 2009. Grammar supported Facade Reconstruction from Mobile LiDAR Mapping. In: ISPRS Workshop on Object Extraction for 3D City Models, Road Databases and Traffic Monitoring. Paris, France.
• 3. Besl, P. J. and MacKay, N., 1992. A method for registration of 3-D shapes. IEEE Transactions on Pattern Analysis and Machine Intelligence, 14(2), pp. 239-256.
• 4. Burrus, N., 2011. Demo software to visualize, calibrate and process Kinect cameras output: http://nicolas.burrus.name/index.php/Research/KinectRgbDemoV5?from=Research.KinectRgbDemoV4 (Accessed 1 Apr. 2011)
• 5. Fietz, A., Jakisch, S.M., Visel, B.A., Fritsch, D., 2010. Automated 2D Measuring of Interiors Using a Mobile Platform. In: Proceedings of the 7th International Conference on Informatics in Control, Automation and Robotics (ICINCO 2010), Funchal, Madeira/Portugal.
• 6. Godha, S. & Lachapelle, G., 2008. Foot mounted inertial system for pedestrian navigation. Measurement Science and Technology, 19(7), 075202.
• 7. Henry, P., Krainin, M., Herbst, E., Renand, X., Fox, D., 2010. RGB-D Mapping: Using Depth Cameras for Dense 3D Modeling of Indoor Environments, RSS Workshop on Advanced Reasoning with Depth Cameras.
• 8. Lowe, D. G., 2004. Distinctive image features from scale-invariant keypoints. International journal of computer vision, 60(2), pp. 91-110.
• 9. Neufert, E. et al., 2002. Architects’ Data 3rd ed., Wiley-Blackwell.
• 10. OpenKinect, 2011. OpenKinect open source project: http://openkinect.org/wiki/Main_Page (Accessed 1 Apr. 2011)
• 11. OpenStreetMap Wiki, 2011. Beginners’ guide - OpenStreetMap Wiki.: http://wiki.openstreetmap.org/wiki/Beginners%27_Guide (Accessed 1 Apr. 2011)
• 12. Peter, M., Haala, N., Schenk, M. & Otto, T., 2010. Indoor Navigation and Modeling Using Photographed Evacuation Plans and MEMS IMU. IAPRS, Vol. XXXVIII, Part 4, on CD.
• 13. ROS OpenNI Kinect, 2011. ROS OpenNI open source project: http://www.ros.org/wiki/openni_kinect (Accessed 1 Apr. 2011)
• 14. Suzuki, S. et al., 1985. Topological structural analysis of digitized binary images by border following. Computer Vision, Graphics, and Image Processing, 30(1), pp. 32-46.
• 15. Yin, X., Wonka, P. & Razdan, A., 2009. Generating 3D Building Models from Architectural Drawings: A Survey. IEEE Computer Graphics and Applications, 29(1), pp. 20-30.
• 16. Zhang, T. Y. & Suen, C. Y., 1984. A fast parallel algorithm for thinning digital patterns. Communications of the ACM, 27(3), pp. 236-239.