Abstract
This paper proposes a novel distributed EKF-SLAM system that combines the advantages of EKF-SLAM and distributed SLAM systems. The system model of this novel SLAM system has a distributed structure, and each subsystem is a special SLAM system corresponding to every effectively observed landmark by feeding the heading information from a magnetic compass is introduced into the observation equation. Aim at the correlation problem in distributed SLAM system, a decorrelated distributed EKF (DDEKF) was developed to estimate the robot pose and landmarks. DDEKF reconstructs and extends the maximum allocation covariance (MAC) method so that it can be applied to the distributed structure where the number of local filters is dynamically changed. Then, the local filter estimation results are weighted and fused in the main filter to obtain the estimation result. Finally, the experimental tests were performed in an outdoor environment, and the experiment results demonstrate that the proposed novel distributed EKF-SLAM system has a better performance than the existing SLAM system.
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Durrant, H., Bailey, T.: Simultaneous localization and mapping: part I. IEEE Robot. Autom. Mag. 13(2), 99–110 (2006)
Caballero, F., Merino, L., Ferruz, J., Ollero, A.: Vision-Based Odometry and SLAM for Medium and High Altitude Flying UAVs. J Intell Robot Syst. 54(3), 137–161 (2009)
Wang, C.L., Wang, T.M., Liang, J.H., Zhang, Y.C., Zhou, Y.: Bearing-only Visual SLAM for Small Unmanned Aerial Vehicles in GPS-denied Environments. Int. J. Autom. Comput. 10(5), 387–396 (2013)
Mallios, A., Ridao, P., Ribas, D., Hernández, E.: Scan matching SLAM in underwater environments. Auton. Robot. 36(3), 181–198 (2014)
Lee, C.S., Nagappa, S., Palomeras, N., Clark, D.E., Salvi, J.: SLAM with SC-PHD Filters: An Underwater Vehicle Application. IEEE Robot. Autom. Mag. 21(2), 38–45 (2014)
Tong, C.H., Barfoot, T.D.: Three-dimensional SLAM for mapping planetary worksite. J Field Rob. 29(3), 381–412 (2012)
Frank, D., Michael, K.: Square root SAM: Simultaneous location and mapping via square root information smoothing. Int. J. Robot. Res. 25(12), 1181–1203 (2006)
Kaess, M., Ranganathan, A., Dellaert, F.: iSAM: Incremental smoothing and mapping. IEEE Trans. Robot. 24(6), 1365–1378 (2008)
Hochbaum, D.S.: Complexity and algorithms for convex network optimization and other nonlinear problems[J]. 4OR. 3(3), 171–216 (2005)
Grisetti, G., Kümmerle, R., Stachniss, C., Frese, U., Hertzberg, C.: Hierarchical optimization on manifolds for online 2D and 3D mapping, inProc. IEEE Int. Conf. Robot. Autom., pp. 273–278 (2010)
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 Trans. Robot. Autom. 17(3), 229–241 (2001)
Yadkuri, F.F., Khosrowjerdi, M.J.: Methods for Improving the Linearization Problem of Extended Kalman Filter. J Intell Robot Syst. 78(3), 485–497 (2014)
Jia, S.M., Wang, K., Li, X.Z.: Mobile Robot Simultaneous Localization and Mapping Based on a Monocular Camera, Journal of Robotics, vol.2016, Article ID 7630340, 11 pages, (2016)
Thrun, S., Liu, Y., Koller, D., Ng, A.Y., Ghahramani, Z., Durrant Whyte, H.: Simultaneous localization and mapping with sparse extended information filters. Int. J. Robot. Res. 23(7–8), 693–716 (2004)
Walter, M.R., Eustice, R.M., Leonard, J.J.: Exactly sparse extended information filters for feature-based SLAM. Int. J. Robot. Res. 26(4), 335–359 (2007)
Montemerlo, M.: FastSLAM: A factored solution to the simultaneous localization and mapping problem with unknown data association, PhD thesis, Carnegie Mellon University, (2003)
Kim, C., Sakthivel, R., Chung, W.K.: Unscented FastSLAM: A robust and efficient solution to the SLAM problem. IEEE Trans Robot. 24(4), 808–820 (2008)
Ramaza, H., Hamid, D.T., Mohammad, A.N., Mohammad, T.: A square root unscented FastSLAM with improved proposal distribution and resamplin. IEEE Trans Ind Electron. 61(5), 2334–2345 (2014)
Thrun, S., Montemerlo, M., Koller, D., Wegbreit, B., Nieto, J., Nebot, E.: FastSLAM using compressed occupancy grids, Journal of Sensors, vol.2016, Article ID 3891865, 23 pages, (2016)
Won, D., Chun, S., Sung, S.: Improving mobile robot navigation performance using vision based SLAM and distributed filters, International Conference on Control, Automation and Systems, 186–191, (2008)
Won, D., Chun, S., Sung, S., Lee, Y., Cho, J., Joo, J., Park, J.: INS/vSLAM system using distributed particle filter, International Journal of Control, Automation, and Systems, pp.1232–1240, (2010)
Pei, F-J, Wu, M., Zhang, S.: Distributed SLAM Using Improved Particle Filter for Mobile Robot Localization, The Scientific World Journal, 86, Paper https://doi.org/10.1155/2014/239531, (2014)
Pei, F.-j., Cheng, Y.-h., Li, H.: Distributed Simultaneous location and mapping based on partition of space-rigion. Syst Eng Electron. 37(3), 639–645 (2015)
Zarei-Jalalabadi, M., Malaek, S.M.B., Kia, S.S.: A Track-to-Track Fusion Method for Tracks With Unknown Correlations[J]. IEEE Control Syst Lett. 2(2), 189–194 (2018)
Barshalom Y.: Multitarget-Multisensor Tracking: Aplications and Advances[M]// Multitarget-multisensor tracking : advanced applications. Artech House, (1993)
Vidal-Calleja, T., Bryson, M., Sukkarieh, S.: On the Observability of Bearing-only SLAM, IEEE International Conference on Robotics and Automation, Roma, Italy, 10–14 April (2007)
Goshen-Meskin, D., Bar-Itzhack, I.: Observability analysis of piece-wise constant systems - Part I: Theory. IEEE Trans Aerosp Electron Syst. 28(4), 1056–1067 (Oct. 1992)
Chong, C.Y., Allen, B., Hamilton, I. et al. Convex combination and covariance intersection algorithms in distributed fusion[C]// Proc. Fusion'01. (2001)
Matzka, S., Altendorfer, R.: A Comparison of Track-to-Track Fusion Algorithms for Automotive Sensor Fusion[M]// Multisensor Fusion and Integration for Intelligent Systems. Springer Berlin Heidelberg, 69–81, (2009)
Fong, L.W.: Multi-sensor track-to-track fusion via linear minimum variance sense estimators[J]. Asian J Control. 10(3), 277–290 (2008)
Acknowledgments
This work was supported by the National Science Foundation of Beijing Municipality under Grant 4162011, the Program for the Education Scientific Research of Beijing KM201610005009, and the 2017 BJUT United Grand Scientific Research Program on Intelligent Manufacturing 040000546317552.
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Pei, F., Zhu, M. & Wu, X. A Decorrelated Distributed EKF-SLAM System for the Autonomous Navigation of Mobile Robots. J Intell Robot Syst 98, 819–829 (2020). https://doi.org/10.1007/s10846-019-01069-z
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DOI: https://doi.org/10.1007/s10846-019-01069-z