Hostname: page-component-7479d7b7d-8zxtt Total loading time: 0 Render date: 2024-07-13T13:30:39.776Z Has data issue: false hasContentIssue false
  • English
  • Français

WiFi SLAM algorithms: an experimental comparison

Published online by Cambridge University Press:  18 July 2014

F. Herranz ,
A. Llamazares ,
E. Molinos ,
M. Ocaña  and
M. A. Sotelo
F. Herranz*
Affiliation:
Department of Electronics, Polytechnic School, University Campus, University of Alcalá, 28871 Alcalá de Henares, Madrid, Spain
A. Llamazares
Affiliation:
Department of Electronics, Polytechnic School, University Campus, University of Alcalá, 28871 Alcalá de Henares, Madrid, Spain
E. Molinos
Affiliation:
Department of Electronics, Polytechnic School, University Campus, University of Alcalá, 28871 Alcalá de Henares, Madrid, Spain
M. Ocaña
Affiliation:
Department of Electronics, Polytechnic School, University Campus, University of Alcalá, 28871 Alcalá de Henares, Madrid, Spain
M. A. Sotelo
Affiliation:
Computer Engineering Department, Polytechnic School, University Campus, University of Alcalá, 28871 Alcalá de Henares, Madrid, Spain
*
*Corresponding author. E-mail: fernando.herranz@depeca.uah.es
Get access Rights & Permissions [Opens in a new window]

Summary

Localization and mapping in indoor environments, such as airports and hospitals, are key tasks for almost every robotic platform. Some researchers suggest the use of Range-Only (RO) sensors based on WiFi (Wireless Fidelity) technology with SLAM (Simultaneous Localization And Mapping) techniques to solve both problems. The current state of the art in RO SLAM is mainly focused on the filtering approach, while the study of smoothing approaches with RO sensors is quite incomplete. This paper presents a comparison between filtering algorithms, such as EKF and FastSLAM, and a smoothing algorithm, the SAM (Smoothing And Mapping). Experimental results are obtained in indoor environments using WiFi sensors. The results demonstrate the feasibility of the smoothing approach using WiFi sensors in an indoor environment.

Keywords

WiFiSLAMSAMIndoor
Type
Articles
Information
Robotica , Volume 34 , Issue 4 , April 2016 , pp. 837 - 858
Copyright
Copyright © Cambridge University Press 2014 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

1.Bahl, P. and Padmanabhan, V. N., “RADAR: An In-Building RF-Based User Location and Tracking System,” Proceedings of the IEEE Infocom 2000, vol. 2 (IEEE Computer and Communications Societies, Tel-Aviv, Israel, 2000) pp. 775–784.Google Scholar
2.Ocaña, M., Bergasa, L. M., Sotelo, M. Á., Flores, R. F., Llorca, D. F. and Schleicher, D., “Automatic training method applied to a WiFi+ultrasound POMDP navigation system,” Robotica 27 (07), 1049–1061 (Dec. 2009).CrossRefGoogle Scholar
3.Matellán, V., Cañas, J. M. and Serrano, O., “WiFi localization methods for autonomous robots,” Robotica 24 (4), 455–461 (2006).Google Scholar
4.Thrun, S., Burgard, W. and Fox, D., Probabilistic Robotics (Intelligent Robotics and Autonomous Agents) (MIT Press, Cambridge, Massachusetts, 2005).Google Scholar
5.Montemerlo, M., Thrun, S., Koller, D. and Wegbreit, B., “FastSLAM: A Factored Solution to the Simultaneous Localization and Mapping Problem,” Proceedings of the AAAI National 2002 (Conference on Artificial Intelligence, Edmonton, Canada, 2002) pp. 593–598.Google Scholar
6.Thrun, S., Burgard, W., Fox, D.et al., Probabilistic Robotics, vol. 1 (MIT Press, Cambridge, Massachusetts, 2005).Google Scholar
7.Dellaert, F. and Kaess, M., “Square root SAM: Simultaneous localization and mapping via square root information smoothing,” Int. J. Robot. Res. 25 (12), 1181–1203 (2006).CrossRefGoogle Scholar
8.Djugash, J. and Singh, S., “A Robust Method of Localization and Mapping Using Only Range,” Proceedings of the International Symposium on Experimental Robotics 2008 (Springer Tracts in Advanced Robotics, Athens, Greece, 2008) pp. 341–351.CrossRefGoogle Scholar
9.Djugash, J., Hamner, B. and Roth, S., “Navigating with ranging radios: Five data sets with ground truth,” J. Field Robot. 26 (1), 689–695 (Sep. 2009).CrossRefGoogle Scholar
10.Djugash, J., Singh, S. and Corke, P., “Further Results with Localization and Mapping Using Range from Radio,” Proceedings of the International Conference on Field & Service Robotics 2005 (Springer Tracts in Advanced Robotics, Port Douglas, Australia, 2005) pp. 231–242.CrossRefGoogle Scholar
11.Blanco, J.-L., González, J. and Fernández-Madrigal, J.-A., “A Pure Probabilistic Approach to Range-only SLAM,” Proceedings of the Internaitonal Conference on Robotics and Automation, Pasadena, USA (May 19–23, 2008) pp. 1436–1441.CrossRefGoogle Scholar
12.Blanco, J. L., Fernandez-Madrigal, J. A. and Gonzalez, J., “Efficient Probabilistic Range-only SLAM,” Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems (IEEE Robotics and Automation Society, Nice, France, 2008) pp. 1017–1022.CrossRefGoogle Scholar
13.Ferris, B., Fox, D. and Lawrence, N., “Wifi-slam Using Gaussian Process Latent Variable Models,” Proceedings of the 20th International Joint Conference on Artificial Intelligence 2007, vol. 7 (Hyderabad, India, 2007) pp. 2480–2485.Google Scholar
14.Huang, J., Millman, D., Quigley, M., Stavens, D., Thrun, S. and Aggarwal, A., “Efficient, Generalized Indoor WiFi Graphslam,” Proceedings of the IEEE International Conference on Robotics and Automation 2011 (IEEE Robotics and Automation Society, Shanghai, China, 2011) pp. 1038–1043.CrossRefGoogle Scholar
15.Kotanen, A., Hannikainen, M., Leppakoski, H. and Hamalainen, T., “Positioning with Ieee 802.11b Wireless Lan,” Proceedings of the 14th IEEE Conference on Personal, Indoor and Mobile Radio Communications 2003, vol. 3 (IEEE, Beijing, China, 2003) pp. 2218–2222.Google Scholar
16.Herranz, F., Simultaneous Localization and Mapping Using Range Only Sensors, Ph.D. Dissertation (Alcalá de Henares, Spain: University of Alcalá, Sep. 2013).Google Scholar
17.Leonard, J. J. and Durrant-Whyte, H. F., “Simultaneous Map Building and Localization for an Autonomous Mobile Robot,” Proceedings of the IEEE/RSJ International Workshop on Intelligent Robots and Systems 1991 (IEEE Robotics and Automation Society, Osaka, Japan, 1991) pp. 1442–1447.Google Scholar
18.Olson, E., Leonard, J. and Teller, S., “Robust Range-only Beacon Localization,” Proceedings of the IEEE/OES Conference on Autonomous Underwater Vehicles 2004 (IEEE Oceanic Engineering Society, 2004) pp. 66–75.CrossRefGoogle Scholar
19.Herranz, F., Ocaña, M., Bergasa, L., Hernández, N., Llamazares, A. and Fernández, C., “Mapping Based on a Noisy Range-Only Sensor,” In: Computer Aided Systems Theory–EUROCAST 2011 (Moreno-Diaz, R., Pichler, F. and Quesada-Arencibia, A.) (Springer Berlin Heidelberg, 2012) pp. 420–425.CrossRefGoogle Scholar
20.Mardia, K. V., “Measures of multivariate skewness and kurtosis with applications,” Biometrika 57 (3), 519–530 (1970).CrossRefGoogle Scholar
21.Montemerlo, M., Thrun, S., Koller, D. and Wegbreit, B., “FastSLAM: A Factored Solution to the Simultaneous Localization and Mapping Problem,” Proceedings of the National Conference on Artificial Intelligence 2002 (Conference on Artificial Intelligence, Edmonton, Canada, 2002) pp. 593–598.Google Scholar
22.Kschischang, F. R., Frey, B. J. and Loeliger, H.-A., “Factor graphs and the sum-product algorithm,” IEEE Trans. Inf. Theory 47, 498–519 (1998).CrossRefGoogle Scholar
23.Eustice, R. M., Singh, H. and Leonard, J. J., “Exactly Sparse Delayed-state Filters,” Proceedings of the IEEE International Conference on Robotics and Automation 2005 (IEEE Robotics and Automation Society, Barcelona, Spain, 2005) pp. 2417–2424.Google Scholar
24.Davis, T. A., Gilbert, J. R., Larimore, S. I. and Ng, E. G., “A column approximate minimum degree ordering algorithm,” ACM Trans. Math. Softw. 30 (3), 353–376 (Sep. 2004).CrossRefGoogle Scholar
25.Lu, F. and Milios, E., “Globally consistent range scan alignment for environment mapping,” Auton. Robots 4 (4), 333–349 (1997).CrossRefGoogle Scholar
26.Burgard, W., Stachniss, C., Grisetti, G., Steder, B., Kummerle, R., Dornhege, C., Ruhnke, M., Kleiner, A. and Tardós, J., “A Comparison of SLAM Algorithms Based on a Graph of Relations,” Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems 2009 (IEEE Robotics and Automation Society, St. Louis, USA, 2008) pp. 2089–2095.CrossRefGoogle Scholar
27.Grisetti, G., Stachniss, C. and Burgard, W., “Improved techniques for grid mapping with rao-blackwellized particle filters,” IEEE Trans. Robot. 23 (1), 34–46 (2007).CrossRefGoogle Scholar
28.Bailey, T., Nieto, J. and Nebot, E., “Consistency of the Fastslam Algorithm,” Proceedings of the IEEE International Conference on Robotics and Automation 2006 (IEEE Robotics and Automation Society, Orlando, USA, 2005) pp. 424–429.Google Scholar
29.Paz, L. M., Tardos, J. and Neira, J., “Divide and conquer: EKF SLAM in O(n),” IEEE Trans. Robot. 24 (5), 1107–1120 (Oct. 2008).CrossRefGoogle Scholar
30.Kaess, M., Ranganathan, A. and Dellaert, F., “iSAM: Incremental smoothing and mapping,” IEEE Trans. Robot. 24 (6), 1365–1378 (2008).CrossRefGoogle Scholar