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Autonomous Robots

, Volume 38, Issue 3, pp 243–259 | Cite as

Feature-based map merging with dynamic consensus on information increments

  • Rosario AraguesEmail author
  • Carlos Sagues
  • Youcef Mezouar
Article

Abstract

We study the problem of feature-based map merging in robot networks. Along its operation, each robot observes the environment and builds and maintains a local map. Simultaneously, each robot communicates and computes the global map of the environment. The communication between the robots is range-limited. Our contributions are the proposal and careful study of the properties of an algorithm that considers separately robot poses and features positions, and that reaches consensus on the latest global map using the map increments between the previous and the current time steps. We give proofs of unbiasedness and consistency of this global map for all the robots, at each iteration. Our algorithm is fully distributed and does not rely on any particular communication topology. Under mild connectivity conditions on the communication graph, our merging algorithm asymptotically converges to the global map. The proposed approach has been experimentally validated using real RGB-D images.

Keywords

Distributed robot systems Networked robots Distributed sensor fusion Mapping 

Notes

Acknowledgments

This work was supported by Grants from the French program investissement d’avenir managed by the National Research Agency (ANR), the European Commission (Auvergne FEDER funds) and the Région Auvergne in the framework of the LabEx IMobS3 (ANR-10-LABX-16-01) and by projects from the Spanish Government DPI2009-08126, and DPI2012-32100.

References

  1. Alriksson, P., & Rantzer, A. (July 2006). Distributed Kalman filtering using weighted averaging. In International Symposium on Mathematical Theory of Networks and Systems. Kyoto.Google Scholar
  2. Aragues, R., Carlone, L., Calafiore, G., & Sagues, C. (May 2011). Multi-agent localization from noisy relative pose measurements. In IEEE International Conference on Robotics and Automation (pp. 364–369). Shanghai.Google Scholar
  3. Aragues, R., Cortes, J., & Sagues, C. (2011). Distributed consensus algorithms for merging feature-based maps with limited communication. Robotics and Autonomous Systems, 59(3–4), 163–180.CrossRefGoogle Scholar
  4. Aragues, R., Cortes, J., & Sagues, C. (2012). Distributed consensus on robot networks for dynamically merging feature-based maps. IEEE Transactions on Robotics, 28(4), 840–854.Google Scholar
  5. Aragues, R., Cortes, J., & Sagues, C. (July 2013). Distributed map merging with consensus on the common part. European Control Conference (pp. 736–741). Switzerland: Zurich.Google Scholar
  6. Aragues, R., Montijano, E., & Sagues, C. (June 2010). Consistent data association in multi-robot systems with limited communications. Robotics: Science and Systems (pp. 97–104). Spain: Zaragoza.Google Scholar
  7. Aragues, R., Sagues, C., & Mezouar, Y. (May 2013). Feature-based map merging with dynamic consensus on information increments. In IEEE International Conference on Robotics and Automation (pp. 2710–2715). Karlsruhe.Google Scholar
  8. Aragues, R., Shi, G., Dimarogonas, D. V., Sagues, C., & Johansson, K. H. (June 2012). Distributed algebraic connectivity estimation for adaptive event-triggered consensus. American Control Conference (pp. 32–37). Canada: Montreal.Google Scholar
  9. Bar-Shalom, Y., Li, X. R., & Kirubarajan, T. (2004). Estimation with applications to tracking and navigation: Theory algorithms and software. New York: John Wiley & Sons.Google Scholar
  10. Calafiore, G., & Abrate, F. (2009). Distributed linear estimation over sensor networks. International Journal of Control, 82(5), 868–882.CrossRefzbMATHMathSciNetGoogle Scholar
  11. Carli, R., Chiuso, A., Schenato, L., & Zampieri, S. (2008). Distributed Kalman filtering based on consensus strategies. IEEE Journal on Selected Areas in Communications, 26(4), 622–633.CrossRefGoogle Scholar
  12. Casbeer, D. W., & Beard, R. (June 2009). Distributed information filtering using consensus filters. American Control Conference (pp. 1882–1887). USA: St. Louis.Google Scholar
  13. Cunningham, A., Indelman, V., & Dellaert, F. (May 2013). DDF-SAM 2.0: Consistent distributed smoothing and mapping. In IEEE International Conference on Robotics and Automation (pp. 5220–5227). Karlsruhe.Google Scholar
  14. Cunningham, A., Wurm, K. M., Burgard, W., & Dellaert, F. (May 2012). Fully distributed scalable smoothing and mapping with robust multi-robot data association. In IEEE International Conference on Robotics and Automation (pp. 1093–1100). St. Paul.Google Scholar
  15. Dissanayake, G., Newman, P., Clark, S., Durrant-Whyte, H. F., & Csorba, M. (2001). A solution to the simultaneous localization and map building (SLAM) problem. IEEE Transactions on Robotics and Automation, 17(3), 229–241.CrossRefGoogle Scholar
  16. Durham, J. W., Franchi, A., & Bullo, F. (2012). Distributed pursuit-evasion without mapping or global localization via local frontier. Autonomous Robots, 32(1), 81–95.Google Scholar
  17. Franceschelli, M., & Gasparri, A. (May 2010). On agreement problems with gossip algorithms in absence of common reference frames. In IEEE International Conference on Robotics and Automation (pp. 4481–4486). Anchorage.Google Scholar
  18. Freeman, R. A., Yang, P., & Lynch, K. M. (December 2006). Stability and convergence properties of dynamic average consensus estimators. In IEEE Conference on Decision and Control (pp. 398–403). San Diego.Google Scholar
  19. Gasparri, A., Fiorini, F., Di Rocco, M., & Panzieri, S. (2012). A networked transferable belief model approach for distributed data aggregation. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, 42(2), 391–405.Google Scholar
  20. Grime, S., & Durrant-Whyte, H. F. (1994). Data fusion in decentralized sensor networks. Control Engineering Practice, 2(5), 849–863.CrossRefGoogle Scholar
  21. Guerrero, J. J., Murillo, A. C., & Sagues, C. (2008). Localization and matching using the planar trifocal tensor with bearing-only data. IEEE Transactions on Robotics, 24(2), 494–501.CrossRefGoogle Scholar
  22. Horn, R. A., & Johnson, C. R. (1985). Matrix Analysis. Cambridge, UK: Cambridge University Press.CrossRefzbMATHGoogle Scholar
  23. Howard, A. (2006). Multi-robot simultaneous localization and mapping using particle filters. International Journal of Robotics Research, 25(12), 1243–1256.CrossRefGoogle Scholar
  24. Huang, G. P., Trawny, N., Mourikis, A. I., & Roumeliotis, S. I. (June 2009). On the consistency of multi-robot cooperative localization. Robotics: Science and Systems (pp. 65–72). WA, USA: Seattle.Google Scholar
  25. Huang, G. P., Trawny, N., Mourikis, A. I., & Roumeliotis, S. I. (2011). Observability-based consistent ekf estimators for multi-robot cooperative localization. Autonomous Robots, 30(1), 99–122.CrossRefGoogle Scholar
  26. Huang, S., Wang, Z., Dissanayake, G., & Frese, U. (2009). Iterated d-slam map joining: Evaluating its performance in terms of consistency, accuracy and efficiency. Autonomous Robots, 27(4), 409–429.CrossRefGoogle Scholar
  27. Indelman, V., Nelson, E., Michael, N., & Dellaert, F. (May 2014). Multi-robot pose graph localization and data association from unknown initial relative poses via expectation maximization. In IEEE International Conference on Robotics and Automation (pp. 593–600). Hong Kong.Google Scholar
  28. Julier, S., & Uhlmann, J. K. (2001). General decentralised data fusion with covariance intersection (CI). In D. L. Hall & J. Llinas (Eds.), Handbook of Multisensor Data Fusion. Vatican: CRC Press.Google Scholar
  29. Kamal, A. T., Ding, C., Song, B., Farrell, J. A., & Roy-Chowdhury, A. K. (December 2011). A generalized kalman consensus filter for wide-area video networks. In IEEE Conference on Decision and Control (pp. 7863–7869). Orlando.Google Scholar
  30. Knuth, J., & Barooah, P. (May 2012). Collaborative 3D localization of robots from relative pose measurements using gradient descent on manifolds. In IEEE International Conference on Robotics and Automation (pp. 1101–1106). St. Paul.Google Scholar
  31. Knuth, J., & Barooah, P. (May 2013). Collaborative localization with heterogeneous inter-robot measurements by riemannian optimization. In IEEE International Conference on Robotics and Automation (pp. 1526–1531). Karlsruhe.Google Scholar
  32. Leshem, A., & Tong, L. (2005). Estimating sensor population via probabilistic sequential polling. IEEE Signal Processing Letters, 12(5), 395–398.CrossRefGoogle Scholar
  33. Leung, K. Y. K., Barfoot, T. D., & Liu, H. H. T. (October 2010). Decentralized cooperative simultaneous localization and mapping for dynamic and sparse robot networks. In IEEE/RSJ International Conference on Intelligent Robots and Systems (pp. 3554–3561). Taipei.Google Scholar
  34. Li, T., & Zhang, J. F. (2010). Consensus conditions on multi-agent systems with time-varying topologies and stochastic communication noises. IEEE Transactions on Automatic Control, 55(9), 2043–2057.CrossRefGoogle Scholar
  35. Lowe, D. G. (1999). Object recognition from local scale-invariant features. In IEEE International Conference on Computer Vision (pp. 1150–1157).Google Scholar
  36. Lynch, K. M., Schwartz, I. B., Yang, P., & Freeman, R. A. (2008). Decentralized environmental modeling by mobile sensor networks. IEEE Transactions on Robotics, 24(3), 710–724.CrossRefGoogle Scholar
  37. Montijano, E., Aragues, R., & Sagues, C. (2013). Distributed data association in robotic networks with cameras and limited communications. IEEE Transactions on Robotics, 29(6), 1408–1423.CrossRefGoogle Scholar
  38. Navarro, I., & Matía, F. (2012). Distributed orientation agreement in a group of robots. Autonomous Robots, 33(4), 445–465.Google Scholar
  39. Nebot, E. M., Bozorg, M., & Durrant-Whyte, H. F. (1999). Decentralized architecture for asynchronous sensors. Autonomous Robots, 6(2), 147–164.CrossRefGoogle Scholar
  40. Nguyen, C.V., Izadi, S., & Lovell, D. (October 2012). Modeling kinect sensor noise for improved 3d reconstruction and tracking. In International Conference on 3D Imaging, Modeling, Processing, Visualization and Transmission (pp. 524–530). Zurich. Google Scholar
  41. Olfati-Saber, R. (2005). Distributed Kalman filter with embedded consensus filters. In IEEE Conference on Decision and Control (pp. 8179–8184). Sevilla.Google Scholar
  42. Olfati-Saber, R. (December 2007). Distributed Kalman filtering for sensor networks. In IEEE Conference on Decision and Control (pp. 5492–5498). New Orleans.Google Scholar
  43. Olfati-Saber, R., & Shamma, J. S. (2005). Consensus filters for sensor networks and distributed sensor fusion. In IEEE Conference on Decision and Control (pp. 6698–6703). Sevilla.Google Scholar
  44. Paz, L. M., Tardos, J. D., & Neira, J. (2008). Divide and conquer: EKF SLAM in \(o(n)\). IEEE Transactions on Robotics, 24(5), 1107–1120.CrossRefGoogle Scholar
  45. Ren, W. (2007). Consensus seeking in multi-vehicle systems with a time-varying reference state. In American Control Conference (pp. 717–722). New York.Google Scholar
  46. Ren, W., Beard, R. W., & Atkins, E. M. (2007). Information consensus in multivehicle cooperative control. IEEE Control Systems Magazine, 27(2), 71–82.CrossRefGoogle Scholar
  47. Sandell, N. F., & Olfati-Saber, R. (December 2008). Distributed data association for multi-target tracking in sensor networks. In IEEE Conference on Decision and Control (pp. 1085–1090). Cancun.Google Scholar
  48. Spanos, D. P., Olfati-Saber, R., & Murray, R. M. (2005). Dynamic consensus on mobile networks. In ”IFAC World Congress”: Prague, Czech Republic.Google Scholar
  49. Sun, Y. G., Wang, L., & Xie, G. (2008). Average consensus in networks of dynamic agents with switching topologies and multiple time-varying delays. Systems and Control Letters, 57(2), 175–183.CrossRefzbMATHMathSciNetGoogle Scholar
  50. Thrun, S., Liu, Y., Koller, D., Ng, A., & Durrant-Whyte, H. (2004). Simultaneous localisation and mapping with sparse extended information filters. International Journal of Robotics Research, 23(7–8), 693–716.CrossRefGoogle Scholar
  51. Trawny, N., Zhou, X. S., Zhou, K. X., & Roumeliotis, S. I. (2010). Inter-robot transformations in 3-d. IEEE Transactions on Robotics, 26(2), 226–243.CrossRefGoogle Scholar
  52. Tsokas, N. A., & Kyriakopoulos, K. J. (2012). Multi-robot multiple hypothesis tracking for pedestrian tracking. Autonomous Robots, 32(1), 63–79.Google Scholar
  53. Utete, S., & Durrant-Whyte, H. F. (June 1994). Routing for reliability in decentralised sensing networks. American Control Conference, 2, 2268–2272.Google Scholar
  54. Varagnolo, D., Pillonetto, G., & Schenato, L. (2010). Distributed statistical estimation of the number of nodes in sensor networks. In IEEE Conference on Decision and Control (pp. 1498–1503). Atlanta.Google Scholar
  55. Vincent, R., Fox, D., Ko, J., Konolige, K., Limketkai, B., Morisset, B., et al. (2008). Distributed multirobot exploration, mapping, and task allocation. Annals of Mathematics and Artificial Intelligence, 52(1), 229–255.CrossRefzbMATHMathSciNetGoogle Scholar
  56. Williams, S. B., & Durrant-Whyte, H. (May 2002). Towards multi-vehicle simultaneous localisation and mapping. In IEEE International Conference on Robotics and Automation (pp. 2743–2748). Washington.Google Scholar
  57. Xiao, L., Boyd, S., & Lall, S. (April 2006). A space-time diffusion scheme for peer-to-peer least-square estimation. Symposium on Information Processing of Sensor Networks (IPSN) (pp. 168–176). TN, USA: Nashville.Google Scholar
  58. Zhou, X. S., & Roumeliotis, S. I. (2008). Robot-to-robot relative pose estimation from range measurements. IEEE Transactions on Robotics, 24(6), 1379–1393.CrossRefGoogle Scholar
  59. Zhu, M., & Martınez, S. (2010). Discrete-time dynamic average consensus. Automatica, 46(2), 322–329.CrossRefzbMATHMathSciNetGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  • Rosario Aragues
    • 1
    Email author
  • Carlos Sagues
    • 2
  • Youcef Mezouar
    • 1
  1. 1.Institut Pascal, CNRSClermont UniversitéClermont-Ferrand France
  2. 2.Instituto de Investigación en Ingeniería de AragónUniversidad de ZaragozaZaragozaSpain

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