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. 2019 Nov 3;19(21):4782.
doi: 10.3390/s19214782.

Magnetic-Map-Matching-Aided Pedestrian Navigation Using Outlier Mitigation Based on Multiple Sensors and Roughness Weighting

Yong Hun Kim  1 , Min Jun Choi  2 , Eung Ju Kim  3 , Jin Woo Song  4
Affiliations

Magnetic-Map-Matching-Aided Pedestrian Navigation Using Outlier Mitigation Based on Multiple Sensors and Roughness Weighting

Yong Hun Kim et al. Sensors (Basel). .

Abstract

This research proposes an algorithm that improves the position accuracy of indoor pedestrian dead reckoning, by compensating the position error with a magnetic field map-matching technique, using multiple magnetic sensors and an outlier mitigation technique based on roughness weighting factors. Since pedestrian dead reckoning using a zero velocity update (ZUPT) does not use position measurements but zero velocity measurements in a stance phase, the position error cannot be compensated, which results in the divergence of the position error. Therefore, more accurate pedestrian dead reckoning is achievable when the position measurements are used for position error compensation. Unfortunately, the position information cannot be easily obtained for indoor navigation, unlike in outdoor navigation cases. In this paper, we propose a method to determine the position based on the magnetic field map matching by using the importance sampling method and multiple magnetic sensors. The proposed method does not simply integrate multiple sensors but uses the normalization and roughness weighting method for outlier mitigation. To implement the indoor pedestrian navigation algorithm more accurately than in existing indoor pedestrian navigation, a 15th-order error model and an importance-sampling extended Kalman filter was utilized to correct the error of the map-matching-aided pedestrian dead reckoning (MAPDR). To verify the performance of the proposed indoor MAPDR algorithm, many experiments were conducted and compared with conventional pedestrian dead reckoning. The experimental results show that the proposed magnetic field MAPDR algorithm provides clear performance improvement in all indoor environments.

Keywords: importance sampling; indoor navigation; magnetic field map matching; roughness weighting.

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Conflict of interest statement

The authors declare no conflicts of interest.

Figures

Figure 1
Figure 1
Gait characteristics and stance-phase detection.
Figure 2
Figure 2
Magnetic anomaly maps in the corridor ((a) is magnetic anomaly around the foot, and (b) is magnetic anomaly around the waist).
Figure 3
Figure 3
Magnetic-map roughness ((a) is for simple integration case and (b) is for normalized integration case with roughness weighting factors).
Figure 4
Figure 4
Position map-matching errors and outlier distributions for three cases ((Left) foot-mounted sensor, (Middle) waist-mounted sensor, and (Right) multiple sensor case with simple integration).
Figure 5
Figure 5
(a) Represents the number of outliers according to the tuning gain. (b) Shows the outlier when the roughness weighting factors are applied.
Figure 6
Figure 6
Concept of magnetic map-matching algorithm using importance sampling.
Figure 7
Figure 7
Map-matching-aided pedestrian dead reckoning (MAPDR) algorithm block diagram.
Figure 8
Figure 8
Mounted-sensors’ locations and configuration of the experiment.
Figure 9
Figure 9
Comparison of map-matching results based on true path. (a) Foot-mounted single sensor case, (b) waist-mounted single sensor case, (c) multiple sensors without outlier mitigation, and (d) multiple sensors with outlier mitigation.
Figure 10
Figure 10
Experiment with MAPDR on the magnetic map (a) case 1 (b) case 2 (c) case 3 (d) case 4 (e) case 5 (f) case 6.
Figure 10
Figure 10
Experiment with MAPDR on the magnetic map (a) case 1 (b) case 2 (c) case 3 (d) case 4 (e) case 5 (f) case 6.
Figure 11
Figure 11
The experiment result of MAPDR with site 1 partial magnetic map (a) experiment 1 (b) experiment 2.
Figure 12
Figure 12
The experiment result of MAPDR with site 2 partial magnetic map (a) experiment 1 (b) experiment 2.
See this image and copyright information in PMC

References

    1. Jeong N., Hwang H., Matson E.T. Evaluation of low-cost lidar sensor for application in indoor UAV navigation; Proceedings of the 2018 IEEE Sensors Applications Symposium (SAS); Seoul, Korea. 12–14 March 2018; pp. 1–5.
    1. Liu S., Atia M.M., Karamat T.B., Noureldin A. A LiDAR-aided indoor navigation system for UGVs. J. Navig. 2015;68:253–273. doi: 10.1017/S037346331400054X. - DOI
    1. Wang Y.-T., Peng C.-C., Ravankar A., Ravankar A. A Single LiDAR-Based Feature Fusion Indoor Localization Algorithm. Sensors. 2018;18:1294. doi: 10.3390/s18041294. - DOI - PMC - PubMed
    1. Tian Y., Shigaki D., Wang W., Ahn C.-J. A weighted least-squares method using received signal strength measurements for WLAN indoor positioning system; Proceedings of the 2017 20th International Symposium on Wireless Personal Multimedia Communications (WPMC); Bali, Indonesia. 17–20 December 2017; pp. 310–314.
    1. Sohrabi R., Fallah M.A., Lahouti F. Indoor pedestrian navigation fusing WLAN, INS, and map data using pattern matching; Proceedings of the 7’th International Symposium on Telecommunications (IST’2014); Tehran, Iran. 9–11 September 2014; pp. 349–354.

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