Evaluating Indoor Positioning Systems in a Shopping Mall: The Lessons Learned From the IPIN 2018 Competition

Renaudin, Valérie; Ortiz, Miguel; Perul, Johan; Torres-Sospedra, Joaquín; Jiménez, Antonio R.; Pérez-Navarro, Antoni; Mendoza-Silva, Germán Martín; Seco, Fernando; Landau, Yael; Marbel, Revital; Ben‐Moshe, Boaz; Zheng, Xingyu; Ye, Feng; Kuang, Jian; Liu, Yu; Niu, Xiaoji; Landa, Vlad; Hacohen, Shlomi; Shvalb, Nir; Lu, Chuanhua; Uchiyama, Hideaki; Thomas, Diego; Shimada, Atsushi; Ding, Zhenxing; Xu, Feng; Kronenwett, Nikolai; Vladimirov, Blagovest; Lee, So‐Yeon; Cho, Eunyoung; Jun, Sungwoo; Lee, Changeun; Park, Sangjoon; Lee, Yonghyun; Rew, Jehyeok; Park, Changjun; Jeong, Hyeongyo; Han, Jaeseung; Lee, Keumryeol; Zhang, Wenchao; Li, Xianghong; D, Wei; Zhang, Ying; Park, So Young; Park, Chan Gook; Knauth, Stefan; Pipelidis, Georgios; Tsiamitros, Nikolaos; Lungenstrass, Tomas; Morales, Juan Pablo; Trogh, Jens; David, Paul A.; Opiela, Miroslav; Fang, Shih Hau; Tsao, Yu; Chien, Ying-Ren; Yang, Shi Shen; Ye, Shih Jyun; Ali, Muhammad Usman; Hur, Soojung; Park, Yongwan

doi:10.1109/access.2019.2944389

Cited by 71 publications

(55 citation statements)

References 46 publications

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“…To evaluate the performance in a real scenario, we used datasets from a competition held at IPIN 2018 (International Conference on Indoor Positioning and Indoor Navigation) in a shopping mall in France (the dataset [ 59 ] and the work in ref. [ 60 ] available) and IPIN 2019 competition in a research institute in Italy [ 61 ].…”

Section: Discussionmentioning

confidence: 99%

Grid-Based Bayesian Filtering Methods for Pedestrian Dead Reckoning Indoor Positioning Using Smartphones

Opiela

Galčík

2020

Sensors

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Indoor positioning systems for smartphones are often based on Pedestrian Dead Reckoning, which computes the current position from the previously estimated location. Noisy sensor measurements, inaccurate step length estimations, faulty direction detections, and a demand on the real-time calculation introduce the error which is suppressed using a map model and a Bayesian filtering. The main focus of this paper is on grid-based implementations of Bayes filters as an alternative to commonly used Kalman and particle filters. Our previous work regarding grid-based filters is elaborated and enriched with convolution mask calculations. More advanced implementations, the centroid grid filter, and the advanced point-mass filter are introduced. These implementations are analyzed and compared using different configurations on the same raw sensor recordings. The evaluation is performed on three sets of experiments: a custom simple path in faculty building in Slovakia, and on datasets from IPIN competitions from a shopping mall in France, 2018 and a research institute in Italy, 2019. Evaluation results suggests that proposed methods are qualified alternatives to the particle filter. Advantages, drawbacks and proper configurations of these filters are discussed in this paper.

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Section: Discussionmentioning

confidence: 99%

Grid-Based Bayesian Filtering Methods for Pedestrian Dead Reckoning Indoor Positioning Using Smartphones

Opiela

Galčík

2020

Sensors

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“…The idea of the off-site Track was about improving the development of algorithmic techniques: competitors were provided with some signal traces gathered into three building at a university campus and they were asked to estimate the path which had generated those traces. Similar mixes of on-site and off-site Tracks were proposed during the IPIN Competition editions 2016, 2017, and 2018 [9]. In 2018 and 2019, on-site and off-site tracks have taken place in the same indoor environment.…”

Section: Competitions About Positioning Localisation and Navigationmentioning

confidence: 90%

The IPIN 2019 Indoor Localisation Competition—Description and Results

Potortì¹,

Park²,

Crivello³

et al. 2020

IEEE Access

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“…Our ground truth system, similar to indoor localization competitions [21,22], is based on ground truth points [4,5]. The location of these points is measured in advance.…”

Section: Evaluation Methodologymentioning

confidence: 99%

Automatic Calibration of the Step Length Model of a Pocket INS by Means of a Foot Inertial Sensor

Ahmed

Diaz

Domínguez

2020

Sensors

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All non-foot-mounted inertial localization systems have a common challenge: the need for calibrating the parameters of the step length model. The calibration of the parameters of a step length model is key for an accurate estimation of the pedestrian's step length, and therefore, for the accuracy of the position estimation. In a previous work, we provided a proof of concept on how to calibrate step length models with a foot inertial navigation system (INS), i.e., an INS based on an inertial measurement unit (IMU) mounted on the upper front part of the foot. The reason is that the foot INS does not require calibration thanks to the implementation of the strapdown algorithm. The goal of this article is to automatically calibrate the parameters of a step length model of the pocket INS by means of the foot INS. The step length model of the pocket INS has two parameters: the slope and offset of a first-order linear regression that relates the amplitude of the thigh pitch with the user's step length. Firstly, we show that it is necessary to estimate the two parameters of the step length model. Secondly, we propose a method to automatically estimate these parameters by means of a foot INS. Finally, we propose a practical implementation of the proposed method in the pocket INS. We evaluate the pocket INS with the proposed calibration method and we compare the results to the state of the art implementations of the pocket INS. The results show that the proposed automatic calibration method outperforms the previous work, which proves the need for calibrating all the parameters of the step length model of the pocket INS. In this work, we conclude that it is possible to use a foot INS to automatically calibrate all parameters of the step length model of the pocket INS. Since the calibration of the step length model is always needed, our proposed automatic calibration method is a key enabler for using the pocket INS.

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Evaluating Indoor Positioning Systems in a Shopping Mall: The Lessons Learned From the IPIN 2018 Competition

Cited by 71 publications

References 46 publications

Grid-Based Bayesian Filtering Methods for Pedestrian Dead Reckoning Indoor Positioning Using Smartphones

Grid-Based Bayesian Filtering Methods for Pedestrian Dead Reckoning Indoor Positioning Using Smartphones

The IPIN 2019 Indoor Localisation Competition—Description and Results

Automatic Calibration of the Step Length Model of a Pocket INS by Means of a Foot Inertial Sensor

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