Abstract
In rescue missions or law enforcement applications, accurate determination of every team member’s position and providing this information on a map may significantly improve mutual situation awareness and potentially reduce the risk of accidentally harming a team member. Furthermore, it could help keep track of the areas that have been already visited, helping the coordination of the mission at hand.
Whereas in outdoors environments accurate positioning information can be obtained using a GNSS receivers, in indoor or underground environments GNSS signals are strongly disturbed and other means of localization must be called into play. Foot mounted inertial sensors or IMUs have been one of the proposed solutions, but their performance is prone to errors that grow over time. Only when the map of the environment is provided, can these IMUs perform with high accuracy. But building plans or maps of indoor and underground areas are often unavailable, outdated, incomplete and do not reflect furniture or other obstacles that also constraint the pedestrian’s motion. How can a reliable map of an indoor environment be generated?
FootSLAM - Simultaneous Localization and Mapping for pedestrians - is a novel technique based on foot mounted IMUs that measure the pedestrian’s steps while walking. These measurements can be used to generate a map of an environment while determining the pedestrian’s location within that map. FootSLAM was recently extended to FeetSLAM, the multiuser scenario in which the maps obtained by two or more pedestrians are combined to generate a more extensive and accurate map of the environment.
In this paper we elaborate on different deployment scenarios for FootSLAM and its collaborative counterpart in security and emergency applications, yet to be experimentally validated.
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References
Foxlin, E.: Pedestrian tracking with shoe-mounted inertial sensors. IEEE Computer Graphics and Applications 25(6), 38–46 (2005)
Krach, B., Robertson, P., Angermann, M., Khider, M.: Inertial systems based joint mapping and positioning for pedestrian navigation. In: Proc. ION GNSS 2009, Savannah, Georgia, USA (2009)
Woodman, O., Harle, R.: Pedestrian localization for indoor environments. In: Proc. of the UbiComp 2008, Seoul, South Korea (2008)
Beauregard, S., Widyawan, K.M.: Indoor PDR performance enhancement using minimal map information and particle filters. In: Proc. of the IEEE/ION PLANS 2008, Monterey, USA (2008)
Durrant-Whyte, H., Bailey, T.: Simultaneous Localization and Mapping: Part I Tutorial. IEEE Robotics & Automation Magazine (2006)
Howard, A.: Multi-robot Simultaneous Localization and Mapping using Particle Filters. International Journal of Robotics Research 25(12) (2006)
León, A., Barea, R., Bergasa, L., López, E., Ocaña, M., Schleicher, D.: SLAM and Map Merging. Journal of Physical Agents 3(1) (2009)
Lee, H., Lee, S., Lee. S., Lee, T., Kim, D., Park, K., Lee, K., Lee, B.: Comparison and Analysis of Scan Matching Techniques for Cooperative-SLAM. In: 8th International Conference on Ubiquitous Robots and Ambient Intelligence (URAI), Songdo Conventia, Incheon, Korea (2011)
Kleiner, A., Dornhege, C., Dali, S.: Mapping disaster areas jointly: RFID-Coordinated SLAM by Humans and Robots. In: Proceedings of the 2007 IEEE International Workshop on Safety, Security and Rescue Robotics, Rome, Italy (2007)
Robertson, P., Garcia Puyol, M., Angermann, M.: Collaborative Pedestrian Mapping of Buildings Using Inertial Sensors and FootSLAM. In: ION GNSS 2011, Portland, Oregon, USA (2011)
Howe, J.: The Rise of Crowdsourcing. Wired 14(6) (2006)
Brabham, D.: Crowdsourcing as a Model for Problem Solving: An Introduction and Cases. Convergence: The International Journal of Research into New Media Technologies 14(1), 75–90 (2008)
Dao, T., Zhou, Y., Thill, J., Delmelle, E.: Spatio-temporal location modeling in a 3D indoor environment: the case of AEDs as emergency medical devices. International Journal of Geographical Information Science 26(3) (2012)
Robertson, P., Angermann, M., Khider, M.: Improving Simultaneous Localization and Mapping for Pedestrian Navigation and Automatic Mapping of Buildings by using Online Human-Based Feature Labeling. In: Proc. IEEE/ION PLANS 2010, Palm Springs, CA, USA (2010)
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Garcia Puyol, M., Frassl, M., Robertson, P. (2012). Collaborative Mapping for Pedestrian Navigation in Security Applications. In: Aschenbruck, N., Martini, P., Meier, M., Tölle, J. (eds) Future Security. Future Security 2012. Communications in Computer and Information Science, vol 318. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33161-9_9
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DOI: https://doi.org/10.1007/978-3-642-33161-9_9
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