Indoor magnetic pose graph SLAM with Robust Back-End
- Authors
- Jung, J.; Choi, J.; Oh, T.; Myung, H.
- Issue Date
- 2019
- Publisher
- Springer Verlag
- Keywords
- Magnetic field; Pose graph optimization; Robust back-end; SLAM
- Citation
- Advances in Intelligent Systems and Computing, v.751, pp 153 - 163
- Pages
- 11
- Journal Title
- Advances in Intelligent Systems and Computing
- Volume
- 751
- Start Page
- 153
- End Page
- 163
- URI
- https://www.kriso.re.kr/sciwatch/handle/2021.sw.kriso/8406
- DOI
- 10.1007/978-3-319-78452-6_14
- ISSN
- 2194-5357
- 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. ? Springer International Publishing AG, part of Springer Nature 2019.
- Files in This Item
- There are no files associated with this item.
- Appears in
Collections - ETC > 1. Journal Articles
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.