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Journal articles on the topic 'Pedestrian localization'

Author: Grafiati

Published: 4 June 2021

Last updated: 7 February 2022

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1

Niu, Yiru, Zhihua Xu, Ershuai Xu, Gongwei Li, Yuan Huo, and Wenbin Sun. "Monocular Pedestrian 3D Localization for Social Distance Monitoring." Sensors 21, no. 17 (2021): 5908. http://dx.doi.org/10.3390/s21175908.

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Social distancing protocols have been highly recommended by the World Health Organization (WHO) to curb the spread of COVID-19. However, one major challenge to enforcing social distancing in public areas is how to perceive people in three dimensions. This paper proposes an innovative pedestrian 3D localization method using monocular images combined with terrestrial point clouds. In the proposed approach, camera calibration is achieved based on the correspondences between 2D image points and 3D world points. The vertical coordinates of the ground plane where pedestrians stand are extracted from

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2

Ding, Yiming, Zhi Xiong, Wanling Li, Zhiguo Cao, and Zhengchun Wang. "Pedestrian Navigation System with Trinal-IMUs for Drastic Motions." Sensors 20, no. 19 (2020): 5570. http://dx.doi.org/10.3390/s20195570.

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The combination of biomechanics and inertial pedestrian navigation research provides a very promising approach for pedestrian positioning in environments where Global Positioning System (GPS) signal is unavailable. However, in practical applications such as fire rescue and indoor security, the inertial sensor-based pedestrian navigation system is facing various challenges, especially the step length estimation errors and heading drift in running and sprint. In this paper, a trinal-node, including two thigh-worn inertial measurement units (IMU) and one waist-worn IMU, based simultaneous localiz

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Qian, Jiuchao, Yuhao Cheng, Rendong Ying, and Peilin Liu. "A Novel Indoor Localization Method Based on Image Retrieval and Dead Reckoning." Applied Sciences 10, no. 11 (2020): 3803. http://dx.doi.org/10.3390/app10113803.

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Indoor pedestrian localization measurement is a hot topic and is widely used in indoor navigation and unmanned devices. PDR (Pedestrian Dead Reckoning) is a low-cost and independent indoor localization method, estimating position of pedestrians independently and continuously. PDR fuses the accelerometer, gyroscope and magnetometer to calculate relative distance from starting point, which is mainly composed of three modules: step detection, stride length estimation and heading calculation. However, PDR is affected by cumulative error and can only work in two-dimensional planes, which makes it l

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4

Ziolkowski, Robert. "Investigations of driver’s speed at unsignalised pedestrian crossings." MATEC Web of Conferences 262 (2019): 05018. http://dx.doi.org/10.1051/matecconf/201926205018.

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Pedestrian crossings are critical places for road accidents involving pedestrians and motor vehicles. Due to the relation between speed and injury severe the driver’s speed has a crucial impact on pedestrian safety. In Poland the traffic-related death rate of unprotected road users is extremely high comparing to other countries of European Union even though the traffic law regulations require from drivers special attention and slowing down while approaching to the intersection and/or zebra pedestrian crossing area. The goal of the paper is to investigate driver’s speed while approaching to the

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5

Ashraf, Imran, Soojung Hur, and Yongwan Park. "Application of Deep Convolutional Neural Networks and Smartphone Sensors for Indoor Localization." Applied Sciences 9, no. 11 (2019): 2337. http://dx.doi.org/10.3390/app9112337.

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Indoor localization systems are susceptible to higher errors and do not meet the current standards of indoor localization. Moreover, the performance of such approaches is limited by device dependence. The use of Wi-Fi makes the localization process vulnerable to dynamic factors and energy hungry. A multi-sensor fusion based indoor localization approach is proposed to overcome these issues. The proposed approach predicts pedestrians’ current location with smartphone sensors data alone. The proposed approach aims at mitigating the impact of device dependency on the localization accuracy and lowe

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Wang, Yingying, Hu Cheng, Chaoqun Wang, and Max Q. H. Meng. "Pose-Invariant Inertial Odometry for Pedestrian Localization." IEEE Transactions on Instrumentation and Measurement 70 (2021): 1–12. http://dx.doi.org/10.1109/tim.2021.3093922.

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Ortiz, Miguel, Mathieu De Sousa, and Valerie Renaudin. "A New PDR Navigation Device for Challenging Urban Environments." Journal of Sensors 2017 (2017): 1–11. http://dx.doi.org/10.1155/2017/4080479.

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The motivations, the design, and some applications of the new Pedestrian Dead Reckoning (PDR) navigation device, ULISS (Ubiquitous Localization with Inertial Sensors and Satellites), are presented in this paper. It is an original device conceived to follow the European recommendation of privacy by design to protect location data which opens new research toward self-contained pedestrian navigation approaches. Its application is presented with an enhanced PDR algorithm to estimate pedestrian’s footpaths in an autonomous manner irrespective of the handheld device carrying mode: texting or swingin

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8

Liu, Fei, Jian Wang, Jixian Zhang, and Houzeng Han. "An Indoor Localization Method for Pedestrians Base on Combined UWB/PDR/Floor Map." Sensors 19, no. 11 (2019): 2578. http://dx.doi.org/10.3390/s19112578.

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This paper propose a scheme for indoor pedestrian location, based on UWB (Ultra Wideband)/PDR (Pedestrian Dead Reckoning) and Floor Map data. Firstly, a robust algorithm that uses Tukey weight factor and a pathological parameter for UWB positioning is proposed. The ill-conditioned position problem is solved for a scene where UWB anchors are placed on the same elevation of a narrow corridor. Secondly, a heading angle-computed strategy of PDR is put forward. According to the UWB positioning results, the location of pedestrians is mapped to the Floor Map, and 16 possible azimuth directions with 2

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9

Poulose, Alwin, Jihun Kim, and Dong Seog Han. "A Sensor Fusion Framework for Indoor Localization Using Smartphone Sensors and Wi-Fi RSSI Measurements." Applied Sciences 9, no. 20 (2019): 4379. http://dx.doi.org/10.3390/app9204379.

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Sensor fusion frameworks for indoor localization are developed with the specific goal of reducing positioning errors. Although many conventional localization frameworks without fusion have been improved to reduce positioning error, sensor fusion frameworks generally provide a further improvement in positioning accuracy. In this paper, we propose a sensor fusion framework for indoor localization using the smartphone inertial measurement unit (IMU) sensor data and Wi-Fi received signal strength indication (RSSI) measurements. The proposed sensor fusion framework uses location fingerprinting and

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10

Wang, Mei, Nan Duan, Zou Zhou, et al. "Indoor PDR Positioning Assisted by Acoustic Source Localization, and Pedestrian Movement Behavior Recognition, Using a Dual-Microphone Smartphone." Wireless Communications and Mobile Computing 2021 (July 8, 2021): 1–16. http://dx.doi.org/10.1155/2021/9981802.

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In recent years, the public’s demand for location services has increased significantly. As outdoor positioning has matured, indoor positioning has become a focus area for researchers. Various indoor positioning methods have emerged. Pedestrian dead reckoning (PDR) has become a research hotspot since it does not require a positioning infrastructure. An integral equation is used in PDR positioning; thus, errors accumulate during long-term operation. To eliminate the accumulated errors in PDR localisation, this paper proposes a PDR localisation system applied to complex scenarios with multiple bu

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11

Ashraf, Imran, Soojung Hur, and Yongwan Park. "mPILOT-Magnetic Field Strength Based Pedestrian Indoor Localization." Sensors 18, no. 7 (2018): 2283. http://dx.doi.org/10.3390/s18072283.

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An indoor localization system based on off-the-shelf smartphone sensors is presented which employs the magnetometer to find user location. Further assisted by the accelerometer and gyroscope, the proposed system is able to locate the user without any prior knowledge of user initial position. The system exploits the fingerprint database approach for localization. Traditional fingerprinting technology stores data intensity values in database such as RSSI (Received Signal Strength Indicator) values in the case of WiFi fingerprinting and magnetic flux intensity values in the case of geomagnetic fi

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12

Tian, Zengshan, Yue Jin, Mu Zhou, Zipeng Wu, and Ze Li. "Wi-Fi/MARG Integration for Indoor Pedestrian Localization." Sensors 16, no. 12 (2016): 2100. http://dx.doi.org/10.3390/s16122100.

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13

Ullah, Habib, Ahmed B. Altamimi, Muhammad Uzair, and Mohib Ullah. "Anomalous entities detection and localization in pedestrian flows." Neurocomputing 290 (May 2018): 74–86. http://dx.doi.org/10.1016/j.neucom.2018.02.045.

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14

Zhou, Baoding, Qingquan Li, Qingzhou Mao, Wei Tu, and Xing Zhang. "Activity Sequence-Based Indoor Pedestrian Localization Using Smartphones." IEEE Transactions on Human-Machine Systems 45, no. 5 (2015): 562–74. http://dx.doi.org/10.1109/thms.2014.2368092.

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15

Nguyen-Huu, Khanh, and Seon-Woo Lee. "A Multi-Floor Indoor Pedestrian Localization Method Using Landmarks Detection for Different Holding Styles." Mobile Information Systems 2021 (March 1, 2021): 1–15. http://dx.doi.org/10.1155/2021/6617417.

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The pedestrian dead reckoning (PDR) technique is widely used due to its ease of implementation on portable devices such as smartphones. However, the position error that accumulates over time is the main drawback of this technology. In this paper, we propose a fusion method combining a PDR technique and the landmark recognition methods for multi-floor indoor environments using a smartphone in different holding styles. The proposed method attempts to calibrate the position of a pedestrian by detecting whether the pedestrian passes by specific locations called landmarks. Three kinds of landmarks

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16

Tong, Haibin, Ning Xin, Xianli Su, Tengfeng Chen, and Jingjing Wu. "A Robust PDR/UWB Integrated Indoor Localization Approach for Pedestrians in Harsh Environments." Sensors 20, no. 1 (2019): 193. http://dx.doi.org/10.3390/s20010193.

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Wireless sensor networks (WSNs) and the Internet of Things (IoT) have been widely used in industrial, construction, and other fields. In recent years, demands for pedestrian localization have been increasing rapidly. In most cases, these applications work in harsh indoor environments, which have posed many challenges in achieving high-precision localization. Ultra-wide band (UWB)-based localization systems and pedestrian dead reckoning (PDR) algorithms are popular. However, both have their own advantages and disadvantages, and both exhibit a poor performance in harsh environments. UWB-based lo

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17

Zhou, Baoding, Jun Yang, and Qingquan Li. "Smartphone-Based Activity Recognition for Indoor Localization Using a Convolutional Neural Network." Sensors 19, no. 3 (2019): 621. http://dx.doi.org/10.3390/s19030621.

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In the indoor environment, the activity of the pedestrian can reflect some semantic information. These activities can be used as the landmarks for indoor localization. In this paper, we propose a pedestrian activities recognition method based on a convolutional neural network. A new convolutional neural network has been designed to learn the proper features automatically. Experiments show that the proposed method achieves approximately 98% accuracy in about 2 s in identifying nine types of activities, including still, walk, upstairs, up elevator, up escalator, down elevator, down escalator, do

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18

Hsu, Yu-Liang, Jeen-Shing Wang, and Che-Wei Chang. "A Wearable Inertial Pedestrian Navigation System With Quaternion-Based Extended Kalman Filter for Pedestrian Localization." IEEE Sensors Journal 17, no. 10 (2017): 3193–206. http://dx.doi.org/10.1109/jsen.2017.2679138.

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19

Hariyono, Joko, Van-Dung Hoang, and Kang-Hyun Jo. "Moving Object Localization Using Optical Flow for Pedestrian Detection from a Moving Vehicle." Scientific World Journal 2014 (2014): 1–8. http://dx.doi.org/10.1155/2014/196415.

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This paper presents a pedestrian detection method from a moving vehicle using optical flows and histogram of oriented gradients (HOG). A moving object is extracted from the relative motion by segmenting the region representing the same optical flows after compensating the egomotion of the camera. To obtain the optical flow, two consecutive images are divided into grid cells14×14pixels; then each cell is tracked in the current frame to find corresponding cell in the next frame. Using at least three corresponding cells, affine transformation is performed according to each corresponding cell in t

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20

Sung, Kwangjae, Dong Lee, and Hwangnam Kim. "Indoor Pedestrian Localization Using iBeacon and Improved Kalman Filter." Sensors 18, no. 6 (2018): 1722. http://dx.doi.org/10.3390/s18061722.

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21

Zhang, Mingyang, Yingyou Wen, Jian Chen, Xiaotao Yang, Rui Gao, and Hong Zhao. "Pedestrian Dead-Reckoning Indoor Localization Based on OS-ELM." IEEE Access 6 (2018): 6116–29. http://dx.doi.org/10.1109/access.2018.2791579.

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22

Kang, Wonho, and Youngnam Han. "SmartPDR: Smartphone-Based Pedestrian Dead Reckoning for Indoor Localization." IEEE Sensors Journal 15, no. 5 (2015): 2906–16. http://dx.doi.org/10.1109/jsen.2014.2382568.

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23

Huang, Hsieh, Liu, Cheng, Hsu, and Chan. "Multi-Sensor Fusion Approach for Improving Map-Based Indoor Pedestrian Localization." Sensors 19, no. 17 (2019): 3786. http://dx.doi.org/10.3390/s19173786.

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The interior space of large-scale buildings, such as hospitals, with a variety of departments, is so complicated that people may easily lose their way while visiting. Difficulties in wayfinding can cause stress, anxiety, frustration and safety issues to patients and families. An indoor navigation system including route planning and localization is utilized to guide people from one place to another. The localization of moving subjects is a critical-function component in an indoor navigation system. Pedestrian dead reckoning (PDR) is a technology that is widely employed for localization due to t

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24

TREUILLET, SYLVIE, and ERIC ROYER. "OUTDOOR/INDOOR VISION-BASED LOCALIZATION FOR BLIND PEDESTRIAN NAVIGATION ASSISTANCE." International Journal of Image and Graphics 10, no. 04 (2010): 481–96. http://dx.doi.org/10.1142/s0219467810003937.

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The most challenging issue facing the navigation assistive systems for the visually impaired is the instantaneous and accurate spatial localization of the user. Most of the previously proposed systems are based on global positioning system (GPS) sensors. However, the accuracy of low-cost versions is insufficient for pedestrian use. Furthermore, GPS-based systems are confined to outdoor navigation and experience severe signal losts in urban areas. This paper presents a new approach for localizing a person by using a single-body-mounted camera and computer vision techniques. Instantaneous accura

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25

Yang, Xiaolong, Yanmeng Wang, Mu Zhou, and Yiyao Liu. "Pedestrian Motion Learning Based Indoor WLAN Localization via Spatial Clustering." Wireless Communications and Mobile Computing 2018 (2018): 1–10. http://dx.doi.org/10.1155/2018/2571671.

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Applications on Location Based Services (LBSs) have driven the increasing demand for indoor localization technology. The conventional location fingerprinting based localization involves heavy time and labor cost for database construction, while the well-known Simultaneous Localization and Mapping (SLAM) technique requires assistant motion sensors as well as complicated data fusion algorithms. To solve the above problems, a new pedestrian motion learning based indoor Wireless Local Area Network (WLAN) localization approach is proposed in this paper to achieve satisfactory LBS without the demand

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26

Sung, Kwangjae, Hyung Kyu Lee, and Hwangnam Kim. "Pedestrian Positioning Using a Double-Stacked Particle Filter in Indoor Wireless Networks." Sensors 19, no. 18 (2019): 3907. http://dx.doi.org/10.3390/s19183907.

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The indoor pedestrian positioning methods are affected by substantial bias and errors because of the use of cheap microelectromechanical systems (MEMS) devices (e.g., gyroscope and accelerometer) and the users’ movements. Moreover, because radio-frequency (RF) signal values are changed drastically due to multipath fading and obstruction, the performance of RF-based localization systems may deteriorate in practice. To deal with this problem, various indoor localization methods that integrate the positional information gained from received signal strength (RSS) fingerprinting scheme and the moti

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27

Yu, Wen Bin, Peng Li, Zhi Chen, and Chang Li. "PDR-Aided Algorithm with WiFi Fingerprint Matching for Indoor Localization." Applied Mechanics and Materials 701-702 (December 2014): 989–93. http://dx.doi.org/10.4028/www.scientific.net/amm.701-702.989.

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Recently, indoor localization is essential to enable location-based services for many mobile and social network applications. Due to fluctuation of the wireless signal, the accuracy of a simple WiFi fingerprint-based localization is not high. In this paper, we first exploit Pedestrian Dead Reckoning (PDR) technology to overcome the problem of the wireless signal fluctuation, then propose a PDR-aided algorithm with WiFi fingerprint matching for indoor localization, which using the PDR technology aided indoor localization. Experiments show that our algorithm has better accuracy than other indoor

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28

Ceron, Jesus D., Felix Kluge, Arne Küderle, Bjoern M. Eskofier, and Diego M. López. "Simultaneous Indoor Pedestrian Localization and House Mapping Based on Inertial Measurement Unit and Bluetooth Low-Energy Beacon Data." Sensors 20, no. 17 (2020): 4742. http://dx.doi.org/10.3390/s20174742.

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Indoor location estimation is crucial to provide context-based assistance in home environments. In this study, a method for simultaneous indoor pedestrian localization and house mapping is proposed and evaluated. The method fuses a person’s movement data from an Inertial Measurement Unit (IMU) with proximity and activity-related data from Bluetooth Low-Energy (BLE) beacons deployed in the indoor environment. The person’s and beacons’ localization is performed simultaneously using a combination of particle and Kalman Filters. We evaluated the method using data from eight participants who perfor

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29

Kloeden, H., D. Schwarz, R. H. Rasshofer, and E. M. Biebl. "Fusion of cooperative localization data with dynamic object information using data communication for preventative vehicle safety applications." Advances in Radio Science 11 (July 4, 2013): 67–73. http://dx.doi.org/10.5194/ars-11-67-2013.

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Abstract. Cooperative sensors allow for reliable detection, classification and localization of objects in the vehicle's surroundings – even without a line-of-sight contact to the object. The sensor principle is based on a communication signal between the vehicle and a transponder attached to the object of interest – a pedestrian, for example. Thereby, localization information is gathered by measuring the round-trip time-of-flight (RTOF) and evaluating the angle-of-arrival (AOA) of the incident signal. After that, tracking algorithms are used to recover the kinematic state of the object providi

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30

Zhou, Yan, Xianwei Zheng, Ruizhi Chen, Hanjiang Xiong, and Sheng Guo. "Image-Based Localization Aided Indoor Pedestrian Trajectory Estimation Using Smartphones." Sensors 18, no. 1 (2018): 258. http://dx.doi.org/10.3390/s18010258.

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31

Kim, Jooyoung, and Sooyong Lee. "Sensor Information Filter for Enhancing the Indoor Pedestrian Localization Accuracy." Journal of Korea Robotics Society 7, no. 4 (2012): 276–83. http://dx.doi.org/10.7746/jkros.2012.7.4.276.

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32

Zhice Yang, Xiaojun Feng, and Qian Zhang. "Adometer: Push the Limit of Pedestrian Indoor Localization through Cooperation." IEEE Transactions on Mobile Computing 13, no. 11 (2014): 2473–83. http://dx.doi.org/10.1109/tmc.2014.2329855.

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33

Lee, Seungwoo, Byounggeun Kim, Hoon Kim, Rhan Ha, and Hojung Cha. "Inertial Sensor-Based Indoor Pedestrian Localization with Minimum 802.15.4a Configuration." IEEE Transactions on Industrial Informatics 7, no. 3 (2011): 455–66. http://dx.doi.org/10.1109/tii.2011.2158832.

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34

Ashraf, Imran, Soojung Hur, Muhammad Shafiq, Saru Kumari, and Yongwan Park. "GUIDE: Smartphone sensors-based pedestrian indoor localization with heterogeneous devices." International Journal of Communication Systems 32, no. 15 (2019): e4062. http://dx.doi.org/10.1002/dac.4062.

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35

Zhang, Lijia, Mo Cheng, Zhuoling Xiao, Liang Zhou, and Jun Zhou. "Adaptable Map Matching Using PF-net for Pedestrian Indoor Localization." IEEE Communications Letters 24, no. 7 (2020): 1437–40. http://dx.doi.org/10.1109/lcomm.2020.2984036.

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36

Hyo-Sung Ahn and Kwang Hee Ko. "Simple Pedestrian Localization Algorithms Based on Distributed Wireless Sensor Networks." IEEE Transactions on Industrial Electronics 56, no. 10 (2009): 4296–302. http://dx.doi.org/10.1109/tie.2009.2017097.

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37

Huang, He, Kaiyue Qiu, Wei Li, and Dean Luo. "PDR Combined with Magnetic Fingerprint Algorithm for Indoor Positioning." Proceedings 4, no. 1 (2018): 24. http://dx.doi.org/10.3390/ecsa-5-05726.

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Geomagnetism has become a popular technology for indoor positioning, and its accuracy mainly depends on the accuracy of the geomagnetic matching algorithm. Pedestrian dead reckoning technology can calculate the relative position of pedestrians based on sensor information, but only obtain relative position information. According to the advantages and disadvantages of these two techniques, a high-precision GPDR indoor positioning method is proposed, and the improved particle filter algorithm is used to solve the problem of geomagnetic fingerprint fuzzy solution. Finally, a simulation experiment

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38

Khider, Mohammed, Susanna Kaiser, and Patrick Robertson. "A Novel Three Dimensional Movement Model for Pedestrian Navigation." Journal of Navigation 65, no. 2 (2012): 245–64. http://dx.doi.org/10.1017/s0373463311000713.

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In this paper, a Three Dimensional Pedestrian Movement Model (3D-MM) capable of probabilistically representing pedestrian movement in challenging indoor and outdoor localization environments is developed, implemented and evaluated. In the scope of this paper, the model is used to generate a ‘movement’ or a transition for dynamic positioning systems that are based on sequential Bayesian filtering techniques, such as particle filtering. It can also be used to assign weights for particles' movements proposed by sensors in Likelihood Particle Filters implementations. Alternatively, the developed m

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Schwarz, D., R. H. Rasshofer, and E. M. Biebl. "Optimized tracking for cooperative sensor systems in multipath environments." Advances in Radio Science 6 (May 26, 2008): 71–75. http://dx.doi.org/10.5194/ars-6-71-2008.

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Abstract. In a cooperative sensor system for pedestrian protection, a pedestrian and other road users exchange data by means of radio frequency communication. In the proposed system, the pedestrian carries a transponder which is interrogated by a vehicle and sends an anonymous identification (ID) sequence. By decoding the ID, the interrogation unit in the vehicle detects the presence of the transponder. Evaluating the incident wave of the transponder's answer, a localisation is possible. In the proposed localization system, the measurement results can be distorted by multipath propagation. Mul

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Bousdar Ahmed, Dina, Estefania Munoz Diaz, and Juan Jesús García Domínguez. "Novel Multi-IMU Tight Coupling Pedestrian Localization Exploiting Biomechanical Motion Constraints." Sensors 20, no. 18 (2020): 5364. http://dx.doi.org/10.3390/s20185364.

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In this article, we present a novel tight coupling inertial localization system which simultaneously processes the measurements of two inertial measurement units (IMUs) mounted on the leg, namely the upper thigh and the front part of the foot. Moreover, the proposed system exploits motion constraints of each leg link; that is, the thigh and the foot. To derive these constraints, we carry out a motion tracking experiment to collect both ground truth data and inertial measurements from IMUs mounted on the leg. The performance of the tight coupling system is assessed with a data set of approximat

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Otim, Timothy, Luis E. Díez, Alfonso Bahillo, Peio Lopez-Iturri, and Francisco Falcone. "Effects of the Body Wearable Sensor Position on the UWB Localization Accuracy." Electronics 8, no. 11 (2019): 1351. http://dx.doi.org/10.3390/electronics8111351.

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Over the years, several Ultrawideband (UWB) localization systems have been proposed and evaluated for accurate estimation of the position for pedestrians. However, most of them are evaluated for a particular wearable sensor position; hence, the accuracy obtained is subject to a given wearable sensor position. This paper is focused on studying the effects of body wearable sensor positions i.e., chest, arm, ankle, wrist, thigh, forehead, and hand, on the localization accuracy. According to our results, the forehead and the chest provide the best and worst body sensor location for tracking a pede

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Lee, Jung Ho, Beomju Shin, Donghyun Shin, et al. "Surface Correlation-Based Fingerprinting Method Using LTE Signal for Localization in Urban Canyon." Sensors 19, no. 15 (2019): 3325. http://dx.doi.org/10.3390/s19153325.

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The Global Satellite Navigation System (GNSS) used in various location-based services is accurate and stable in outdoor environments. However, it cannot be utilized in an indoor environment because of low signal availability and degradation of accuracy due to the multipath distortion of satellite signals in urban areas. On the contrary, LTE signals are available almost everywhere in urban areas and are quite stable without much variation throughout the year. This is because of the fixed location of base stations and the well-maintained policy of mobile communication service providers. Its vari

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Lim, Jeonghyeok, and Hyungchul Yoon. "Real-time Pedestrian Dynamic-load Localization using Vision-based Motion Sensing." Journal of the Korean Society of Hazard Mitigation 19, no. 7 (2019): 323–30. http://dx.doi.org/10.9798/kosham.2019.19.7.323.

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Xu, Xiangyu, Mei Wang, Liyan Luo, Zhibin Meng, and Enliang Wang. "An Indoor Pedestrian Localization Algorithm Based on Multi-Sensor Information Fusion." Journal of Computer and Communications 05, no. 03 (2017): 102–15. http://dx.doi.org/10.4236/jcc.2017.53012.

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45

Han, Ji-Yong, Jae-Min Jang, and Junghee Han. "On-Time Internal Pedestrian Localization Algorithm Based on Ad-Hoc Networks." Journal of Korea Information and Communications Society 39C, no. 11 (2014): 1000–1008. http://dx.doi.org/10.7840/kics.2014.39c.11.1000.

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Banu, K. Tasleem, K. Supriya, K. Sony, M. Chandana, M. Bhavana, and Baba Fakruddin Ali. "OUTDOOR and INDOOR VISION BASED LOCALIZATION FOR BLIND PEDESTRIAN NAVIGATION ASSISTANCE." International Journal of Engineering Applied Sciences and Technology 04, no. 12 (2020): 715–20. http://dx.doi.org/10.33564/ijeast.2020.v04i12.127.

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XING, Zhiwei. "Simultaneous Localization and Traversable Region Mapping Based on Pedestrian Behavior Learning." Journal of Mechanical Engineering 55, no. 11 (2019): 36. http://dx.doi.org/10.3901/jme.2019.11.036.

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KATO, Yoshihiro, Hikaru NAGANO, Masashi KONYO, and Satoshi TADOKORO. "Correction of Pedestrian Self-Localization using Propagated Vibrations on Lower Limbs." Proceedings of JSME annual Conference on Robotics and Mechatronics (Robomec) 2016 (2016): 1A2–12a1. http://dx.doi.org/10.1299/jsmermd.2016.1a2-12a1.

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Bousdar Ahmed, Dina, Luis Enrique Diez, Estefania Munoz Diaz, and Juan Jesus Garcia Dominguez. "A Survey on Test and Evaluation Methodologies of Pedestrian Localization Systems." IEEE Sensors Journal 20, no. 1 (2020): 479–91. http://dx.doi.org/10.1109/jsen.2019.2939592.

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Garcia Puyol, Maria, Dmytro Bobkov, Patrick Robertson, and Thomas Jost. "Pedestrian Simultaneous Localization and Mapping in Multistory Buildings Using Inertial Sensors." IEEE Transactions on Intelligent Transportation Systems 15, no. 4 (2014): 1714–27. http://dx.doi.org/10.1109/tits.2014.2303115.

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