Magnetic field constraints and sequence-based matching for indoor pose graph SLAM

Jung, Jongdae; Oh, Taekjun; Myung, Hyun

doi:10.1016/j.robot.2015.03.003

Cited by 34 publications

(21 citation statements)

References 23 publications

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“…Magnetic signals can be used to generate loop closures by analysing temporal sequences of measurements (a single magnetic measurement is not spatially unique). This has been demonstrated by Jung et al in robotics [12] and by the authors in PDR [8]. In both cases sub-metre accuracy was achieved, although back-end efficiency was not considered.…”

Section: Introductionmentioning

confidence: 76%

MSGD: Scalable back-end for indoor magnetic field-based GraphSLAM

Gao

Harle

2017

2017 IEEE International Conference on Robotics and Automation (ICRA)

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Simultaneous Localisation and Mapping (SLAM) systems that recover the trajectory of a robot or mobile device are characterised by a front-end and back-end. The front-end uses sensor observations to identify loop closures; the back-end optimises the estimated trajectory to be consistent with these closures. The GraphSLAM framework formulates the back-end problem as a graph-based optimisation on a pose graph. This paper describes a back-end system optimised for very dense sequence-based loop closures. This arises when the frontend generates magnetic loop closures, among other things. Magnetic measurements are fast varying, which is good for localisation, but the requirement for high sampling rates (50 Hz+) produces many more loop closures than conventional systems. To date, however, there is no study optimising GraphSLAM back-end for sequence-based magnetic loop closures. Hence we introduce a novel variant of the Stochastic Gradient Descentbased SLAM algorithm called MSGD (Magnetic-SGD). We use high-accuracy groundtruth system and extensive real datasets to evaluate MSGD against state-of-the-art back-end algorithms. We demonstrate MSGD is at least as good as the best competitor algorithm in terms of quality, while being faster and more scalable.

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Section: Introductionmentioning

confidence: 76%

MSGD: Scalable back-end for indoor magnetic field-based GraphSLAM

Gao

Harle

2017

2017 IEEE International Conference on Robotics and Automation (ICRA)

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“…This is because it is influenced by ferrous objects in the surrounding environment, such as reinforcing steel inside buildings that distorts the earth's magnetic field. [32], [13], [26], [21], [31] combine these spatial features and inertial measurements for positioning. However, it is difficult to exploit these signatures in outdoor environment, where motion is less constrained and the signatures are less informative.…”

Section: Wireless Signal Based Localisation Methodsmentioning

confidence: 99%

iMag: Accurate and Rapidly Deployable Inertial Magneto-Inductive Localisation

Wei

Trigoni

Markham

2018

2018 IEEE International Conference on Robotics and Automation (ICRA)

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Localisation is of importance for many applications. Our motivating scenarios are short-term construction work and emergency rescue. Not only is accuracy necessary, these scenarios also require rapid setup and robustness to environmental conditions. These requirements preclude the use of many traditional methods e.g. vision-based, laser-based, Ultrawide band (UWB) and Global Positioning System (GPS)-based localisation systems. To solve these challenges, we introduce iMag, an accurate and rapidly deployable inertial magnetoinductive (MI) localisation system. It localises monitored workers using a single MI transmitter and inertial measurement units with minimal setup effort. However, MI location estimates can be distorted and ambiguous. To solve this problem, we suggest a novel method to use MI devices for sensing environmental distortions, and use these to correctly close inertial loops. By applying robust simultaneous localisation and mapping (SLAM), our proposed localisation method achieves excellent tracking accuracy, and can improve performance significantly compared with only using an inertial measurement unit (IMU) and MI device for localisation.

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“…The original data were preprocessed using MatLab software, and some invalid data were deleted, such as blank data that cannot be captured by the mobile phone sensor at the beginning of the acquisition data and incomplete data that were not collected after the data collection was stopped. The area between the line segment and the line segment is complemented by Kriging (26) linear interpolation; finally, a complete geomagnetic fingerprint library (as shown in Fig. 10) can be obtained.…”

Section: Experiments Setupmentioning

confidence: 99%