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.
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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).
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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
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DOI: https://doi.org/10.1007/978-3-319-78452-6_14
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