Odometry correction with localization based on landmarkless magnetic map for navigation system of indoor mobile robot

Rahok, Sam Ann; Ozaki, Koichi

doi:10.1109/icara.2000.4803938

Cited by 21 publications

(16 citation statements)

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“…Similarly, a particle-filtering-based engine was designed in [21] to localize and track users with a given geomagnetic fingerprint map. In general, these techniques require special hardware [13,[24][25][26][27] or dense samples of magnetic data to build a fingerprint map with high training overhead [13,21,23,42]. In contrast, FOLLOWME utilizes the features of geomagnetic field without customized hardware and avoids the time-consuming map construction process.…”

Section: Related Workmentioning

confidence: 99%

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Last-Mile Navigation Using Smartphones

Shu

Shin

et al. 2015

Proceedings of the 21st Annual International Conference on Mobile Computing and Networking

154

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Although GPS has become a standard component of smartphones, providing accurate navigation during the last portion of a trip remains an important but unsolved problem. Despite extensive research on localization, the limited resolution of a map imposes restrictions on the navigation engine in both indoor and outdoor environments. To bridge the gap between the end position obtained from legacy navigation services and the real destination, we propose FOLLOWME, a "last-mile" navigation system to enable plugand-play navigation in indoor and semi-outdoor environments. FOLLOWME exploits the ubiquitous, stable geomagnetic field and natural walking patterns to navigate the users to the same destination taken by an earlier traveler. Unlike existing localization and navigation systems, FOLLOWME is infrastructure-free, energyefficient and cost-saving. We implemented FOLLOWME on smartphones, and evaluated it in a four-story campus building with a testing area of 2000m 2 . Our experimental results with 5 users show that 95% of spatial errors during navigation were 2m or less with at least 50% energy savings over a benchmark system.

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Section: Related Workmentioning

confidence: 99%

“…In addition, the impact of mobile objects on the magnetic field is very limited. See [13,[24][25][26][27] for more detail. These local disturbances, stability over time and robustness make the magnetic field a good candidate for trace synchronization in FOLLOWME.…”

Section: Locally Disturbed Yet Stable Magnetic Fieldmentioning

confidence: 99%

Last-Mile Navigation Using Smartphones

Shu

Shin

et al. 2015

Proceedings of the 21st Annual International Conference on Mobile Computing and Networking

154

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“…기존에 구현된 자기장 지도를 이용한 위치 추정 방법에 는 1차원 경로의 자기장 지도를 이용하여 위치를 추정하는 방법 [7][8][9][10]과 시뮬레이션으로 구현된 2차원 평면의 자기장 지도를 이용하여 위치를 추정 하는 방법이 있다 [11,12]. 1차 원 경로에서 위치를 추정하는 방법은 1차원 경로의 자기장 지도 데이터와 일정시간동안 측정되는 자기장의 오차의 합 이 최소가 되는 지점을 이동장치의 위치로 추정 [9]하거나 경로상의 모든 지점에 대한 오차를 확률밀도함수로 나타내 어 최대값을 가지는 지점과 분산을 이용하여 위치를 추정 [10] 자기 센서에서 측정되는 자기장의 왜곡을 모델링한다.…”

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“…1차 원 경로에서 위치를 추정하는 방법은 1차원 경로의 자기장 지도 데이터와 일정시간동안 측정되는 자기장의 오차의 합 이 최소가 되는 지점을 이동장치의 위치로 추정 [9]하거나 경로상의 모든 지점에 대한 오차를 확률밀도함수로 나타내 어 최대값을 가지는 지점과 분산을 이용하여 위치를 추정 [10] 자기 센서에서 측정되는 자기장의 왜곡을 모델링한다.…”

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Position Estimation Using Magnetic Field Map

Kim¹,

Moon²,

Seo³

et al. 2013

Journal of Institute of Control, Robotics and Systems

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Geomagnetic is refracted by building's wall and pillar. Therefore refracted geomagnetic is able to be used as feature point. In a specific space, a mobile device that is equipped with magnetic sensor array measures 3-axis magnetic field for each point. Magnetic field map is acquired by collecting the every sample point in the magnetic field. The measured magnetic field must be calibrated, because each magnetic sensor has a distortion. For this reason, sensor distortion model and sensor calibration method are proposed in this paper. Magnetic field that is measured by mobile device matches magnetic field map. Result of the matching is used for position estimation. This paper implements hardware system for position estimation method using magnetic field map.

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“…Developing effective calibration techniques have recently been interested for robotic systems [1]. They include odometry, 3D camera error detection, active beacons, gyroscope and magnetic compasses [2][3][4][5][6]. Odometry uses the information of positional sensors attached to each actuator to estimate change in position over time.…”

Section: Introductionmentioning

confidence: 99%

Calibration of Mobile Robotic Systems: A Pilot Study

Maddahi

2017

The 4th International Electronic Conference on Sensors and Applications

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Abstract:The type of wheeled mobile robots (WMRs) may affect the level of positional errors during motion. The errors are inevitable, as caused by imperfections in design to fabrication, and need to be rectified using calibration techniques. This paper uses the odometry method to provide improved short-term accuracy with high sampling rates. Odometry is economical and requires fewer landmarks and simpler sensory system to localize WMRs, compared to other existing techniques such as 3D camera error detection. The context introduces an odometry-based method to correct the motion of a WMR with two-wheeled mechanism. Experimental results showed that positional errors were significantly improved after applying outcome of the calibration.

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Odometry correction with localization based on landmarkless magnetic map for navigation system of indoor mobile robot

Cited by 21 publications

References 1 publication

Last-Mile Navigation Using Smartphones

Last-Mile Navigation Using Smartphones

Position Estimation Using Magnetic Field Map

Calibration of Mobile Robotic Systems: A Pilot Study

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