Abstract
In this paper, a method of solving a simultaneous localization and mapping (SLAM) problem is proposed by employing pose graph optimization and indoor magnetic field measurements. The objective of pose graph optimization is to estimate the robot trajectory from the constraints of relative pose measurements. Since the magnetic field in indoor environments is stable in a temporal domain and sufficiently varying in a spatial domain, these characteristics can be exploited to generate the constraints in pose graphs. In this paper two types of constraints are designed, one is for local heading correction and the other for loop closing. For the loop closing constraint, sequence-based matching is employed rather than a single measurement-based one to mitigate the ambiguity of magnetic measurements. To improve the loop closure detection we further employed existing robust back-end methods proposed by other researchers. Experimental results show that the proposed SLAM system with only wheel encoders and a single magnetometer offers comparable results with a reference-level SLAM system in terms of robot trajectory, thereby validating the feasibility of applying magnetic constraints to the indoor pose graph SLAM.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Thrun, S., Burgard, W., Fox, D.: Probabilistic Robotics. The MIT press, Cambridge, MA (2005)
Gozick, B., Subbu, K., Dantu, R., Maeshiro, T.: Magnetic maps for indoor navigation. IEEE Trans. Instrum. Meas. 60(12), 3883–3891 (2011)
Burnett, J., Yaping, P.D.: Mitigation of extremley low frequency magnetic fields from electrical installations in high-rise buildings. Build. Environ. 37, 769–775 (2002)
Angermann, M., Frassl, M., Doniecy, M., Julianyz, B., Robertson, P.: Characterization of the indoor magnetic field for applications in localization and mapping. In: Proceedings of IEEE International Conference on Indoor Positioning and Indoor Navigation (IPIN2012), Sydney, NSW pp. 1–9 (Nov. 2012)
Haverinen, J., Kemppainen, A.: A geomagnetic field based positioning technique for underground mines. In: Proceedings of IEEE International Symposium on Robotic and Sensors Environments (ROSE2011), Montreal, QC pp. 7–12 (Sept. 2011)
Vallivaara, I., Haverinen, J., Kemppainen, A., Roning, J.: Magenetic field-based SLAM method for solving the localization problem in mobile robot floor-cleaning task. In: Proceedings of IEEE International Conference on Advanced Robotics (ICAR2011), Tallinn, Estonia pp. 198–203 (June 2011)
Frassl, M., Angermann, M., Lichtenstern, M., Robertson, P., Julian, B., Doniec, M.: Magnetic maps of indoor environments for precise localization of legged and non-legged locomotion. In: Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS2013), Tokyo, Japan pp. 913–920 (Nov. 2013)
Fernandez-Madrigeal, J., Claraco, J.L.: Simultaneous Localization and Mapping for Mobile Robots : Introduction and Methods. Information Science Reference, Hershey, PA (2013)
Kuemmerle, R., Grisetti, G., Strasdat, H., Konolige, K., Burgard, W.: G20: a general framework for graph optimization. In: Proceedings of IEEE International Conference on Robotics and Automation (ICRA2011), Shanghai, China pp. 3607–3613 (May 2011)
Jung, J., Oh, T., Myung, H.: Magnetic field constraints and sequence-based matching for indoor pose graph slam. Robot. Autonom. Syst. 70, 92–105 (2015)
Sünderhauf, N., Protzel, P.: Switchable constraints for robust pose graph SLAM. In: Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS2012), Vilamoura, Portugal pp. 1879–1884 (Oct. 2012)
Olson, E., Agarwal, P.: Inference on networks of mixtures for robust robot mapping. Int. J. Robot. Res. 32(7), 826–840 (2013)
Grisetti, G., Stachniss, C., Burgard, W.: Improved techniques for grid mapping with rao-blackwellized particle filters. IEEE Trans. Robot. 23(1), 34–46 (2007)
Acknowledgements
This research was supported by a grant from Endowment Project of “Development of fundamental technologies on underwater environmental recognition for long-range navigation and intelligent autonomous underwater navigation” funded by Korea Research Institute of Ships and Ocean Engineering(PES9390).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer International Publishing AG, part of Springer Nature
About this paper
Cite this paper
Jung, J., Choi, J., Oh, T., Myung, H. (2019). Indoor Magnetic Pose Graph SLAM with Robust Back-End. In: Kim, JH., et al. Robot Intelligence Technology and Applications 5. RiTA 2017. Advances in Intelligent Systems and Computing, vol 751. Springer, Cham. https://doi.org/10.1007/978-3-319-78452-6_14
Download citation
DOI: https://doi.org/10.1007/978-3-319-78452-6_14
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-78451-9
Online ISBN: 978-3-319-78452-6
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)