Multi-robot Range-Only SLAM by Active Sensor Nodes for Urban Search and Rescue

  • Dali Sun
  • Alexander Kleiner
  • Thomas M. Wendt
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5399)


To jointly map an unknown environment with a team of autonomous robots is a challenging problem, particularly in large environments, as for example the area of devastation after a disaster. Under such conditions standard methods for Simultaneous Localization And Mapping (SLAM) are difficult to apply due to possible misinterpretations of sensor data, leading to erroneous data association for loop closure. We consider the problem of multi-robot range-only SLAM for robot teams by solving the data association problem with wireless sensor nodes that we designed for this purpose. The memory of these nodes is utilized for the exchange of map data between multiple robots, facilitating loop-closures on jointly generated maps. We introduce RSLAM, which is a variant of FastSlam, extended for range-only measurements and the multi-robot case. Maps are generated from robot odometry and range estimates, which are computed from the RSSI (Received Signal Strength Indication). The proposed method has been extensively tested in USARSim, which serves as basis for the Virtual Robots competition at RoboCup, and by real-world experiments with a team of mobile robots. The presented results indicates that the approach is capable of building consistent maps in presence of real sensor noise, as well as to improve mapping results of multiple robots by data sharing.


Sensor Node Extend Kalman Filter Receive Signal Strength Indication Laser Range Finder Active Sensor Node 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    Balakirsky, S., Scrapper, C., Carpin, S., Lewis, M.: USARSim: providing a framework for multi-robot performance evaluation. In: Proceedings of PerMIS 2006 (2006)Google Scholar
  2. 2.
    Carpin, S., Lewis, M., Wang, J., Balakirsky, S., Scrapper, C.: Bridging the gap between simulation and reality in urban search and rescue. In: Lakemeyer, G., Sklar, E., Sorrenti, D.G., Takahashi, T. (eds.) RoboCup 2006: Robot Soccer World Cup X. LNCS (LNAI), vol. 4434, pp. 1–12. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  3. 3.
    Dissanayake, M.W.M.G., Newman, P., Clark, S., Durrant-Whyte, H.F., Csorba, M.: A solution to the simultaneous localization and map building (slam) problem. IEEE Transactions on Robotics and Automation 17(3), 229–241 (2001)CrossRefGoogle Scholar
  4. 4.
    Djugash, J., Singh, S., Corke, P.: Further results with localization and mapping using range from radio. In: Proc. of the Fifth Int. Conf. on Field and Service Robotics, Pt. Douglas, Australia (2005)Google Scholar
  5. 5.
    Fischler, M.A., Bolles, R.C.: Random sample consensus: A paradigm for model fitting with applications to image analysis and automated cartography. Comm. of the ACM 24, 381–395 (1981)MathSciNetCrossRefGoogle Scholar
  6. 6.
    Grewal, M.S., Weill, L.R., Andrews, A.P.: Global Positioning Systems, Inertial Navigation, and Integration. John Wiley & Sons, Chichester (2001)Google Scholar
  7. 7.
    Hähnel, D., Burgard, W., Fox, D., Fishkin, K., Philipose, M.: Mapping and localization with rfid technology. In: Proc. of the IEEE International Conference on Robotics and Automation (ICRA) (2004)Google Scholar
  8. 8.
    Texas Instruments. Datasheet, cc2420 2.4 ghz ieee 802.15.4 / zigbee-ready rf transceiver, rev swrs041 (2006),
  9. 9.
    Kantor, G., Singh, S., Peterson, R., Rus, D., Das, A., Kumar, V., Pereira, G., Spletzer, J.: Distributed search and rescue with robot and sensor team. In: Proc. of the Int. Conf. on Field and Service Robotics (FSR), pp. 327–332. Sage Publications, Thousand Oaks (2003)Google Scholar
  10. 10.
    Kehagias, A., Djugash, J., Singh, S.: Range-only slam with interpolated range data. Technical Report CMU-RI-TR-06-26, Robotics Institute. Carnegie Mellon University (2006)Google Scholar
  11. 11.
    Kleiner, A., Dornhege, C.: Real-time localization and elevation mapping within urban search and rescue scenarios. Journal of Field Robotics 24(8–9), 723–745 (2007)CrossRefGoogle Scholar
  12. 12.
    Kleiner, A., Steder, B., Dornhege, C., Hoefler, D., Meyer-Delius, D., Prediger, J., Stueckler, J., Glogowski, K., Thurner, M., Luber, M., Schnell, M., Kuemmerle, R., Burk, T., Braeuer, T., Nebel, B.: Robocuprescue - robot league team rescuerobots freiburg (germany). In: RoboCup 2005 (CDROM Proceedings), Team Description Paper, Rescue Robot League, Osaka, Japan (2005)Google Scholar
  13. 13.
    Kleiner, A., Ziparo, V.A.: Robocuprescue - simulation league team rescuerobots freiburg (germany). In: RoboCup 2006 (CDROM Proceedings), Team Description Paper, Rescue Simulation League, Bremen, Germany (2006)Google Scholar
  14. 14.
    Silicon Laboratories. Datasheet, c8051f310/1/2/3/4/5/6/7, rev 1.7 08/06 (2006),
  15. 15.
    Miller, L.E., Wilson, P.F., Bryner, N.P., Francis, Guerrieri, J.R., Stroup, D.W., Klein-Berndt, L.: Rfid-assisted indoor localization and communication for first responders. In: Proc. of the Int. Symposium on Advanced Radio Technologies (2006)Google Scholar
  16. 16.
    Montemerlo, M.: FastSLAM: A Factored Solution to the Simultaneous Localization and Mapping Problem with Unknown Data Association. PhD thesis, Robotics Institute. Carnegie Mellon University, Pittsburgh, PA (July 2003)Google Scholar
  17. 17.
    Olson, E., Leonard, J., Teller, S.: Robust range-only beacon localization. Autonomous Underwater Vehicles, 2004 IEEE/OES, pp. 66–75 (2004)Google Scholar
  18. 18.
    Seidel, S.Y., Rapport, T.S.: 914 mhz path loss prediction model for indoor wireless communications in multi-floored buildings. IEEE Trans. on Antennas and Propagation (1992)Google Scholar
  19. 19.
    Smith, R., Self, M., Cheeseman, P.: Estimating uncertain spatial relationships in robotics. Autonomous Robot Vehicles 1, 167–193 (1988)zbMATHGoogle Scholar
  20. 20.
    Wendt, T.M., Reindl, L.M.: Reduction of power consumption in wireless sensor networks through utilization of wake up strategies. In: 11th WSEAS international Conference on Systems, Crete Island, Greece (2007)Google Scholar
  21. 21.
    Ziparo, V.A., Kleiner, A., Nebel, B., Nardi, D.: Rfid-based exploration for large robot teams. In: Proc. of the IEEE Int. Conf. on Robotics and Automation (ICRA), Rome, Italy (to appear, 2007)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Dali Sun
    • 1
  • Alexander Kleiner
    • 1
  • Thomas M. Wendt
    • 2
  1. 1.Department of Computer SciencesUniversity of FreiburgFreiburgGermany
  2. 2.Department of Microsystems EngineeringUniversity of FreiburgFreiburgGermany

Personalised recommendations