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Journal articles on the topic 'Real-time Localization'

Author: Grafiati
Published: 4 June 2021
Last updated: 14 April 2023

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1

Atiya, S., and G. D. Hager. "Real-time vision-based robot localization." IEEE Transactions on Robotics and Automation 9, no. 6 (1993): 785–800. http://dx.doi.org/10.1109/70.265922.

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2

B. C. Heidman and U. A. Rosa. "Real-Time Tree Localization in Orchards." Applied Engineering in Agriculture 24, no. 6 (2008): 707–16. http://dx.doi.org/10.13031/2013.25360.

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3

Singer, Robert H. "RNA localization: visualization in real-time." Current Biology 13, no. 17 (September 2003): R673—R675. http://dx.doi.org/10.1016/s0960-9822(03)00605-5.

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4

Xin You, Xin You, Daxin Tian Xin You, Chen Liu Daxin Tian, Xiaofeng Yu Chen Liu, and Liangliang Song Xiaofeng Yu. "Vehicles Positioning in Tunnel: A Real-Time Localization System Using DL-TDOA Technology." 網際網路技術學刊 22, no. 5 (September 2021): 965–76. http://dx.doi.org/10.53106/160792642021092205003.

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5

Oyama, D., Y. Adachi, M. Higuchi, J. Kawai, M. Miyamoto, K. Kobayashi, and G. Uehara. "Real-time Head Localization System for Magnetoencephalography." Journal of the Magnetics Society of Japan 36, no. 6 (2012): 345–51. http://dx.doi.org/10.3379/msjmag.1209r003.

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6

Bouvet, Denis, Michel Froumentin, and Gaëtan Garcia. "A real-time localization system for compactors." Automation in Construction 10, no. 4 (May 2001): 417–28. http://dx.doi.org/10.1016/s0926-5805(00)00077-7.

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7

Linåker, F., and M. Ishikawa. "Real-time appearance-based Monte Carlo localization." Robotics and Autonomous Systems 54, no. 3 (March 2006): 205–20. http://dx.doi.org/10.1016/j.robot.2005.11.003.

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8

Lynen, Simon, Bernhard Zeisl, Dror Aiger, Michael Bosse, Joel Hesch, Marc Pollefeys, Roland Siegwart, and Torsten Sattler. "Large-scale, real-time visual–inertial localization revisited." International Journal of Robotics Research 39, no. 9 (July 7, 2020): 1061–84. http://dx.doi.org/10.1177/0278364920931151.

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The overarching goals in image-based localization are scale, robustness, and speed. In recent years, approaches based on local features and sparse 3D point-cloud models have both dominated the benchmarks and seen successful real-world deployment. They enable applications ranging from robot navigation, autonomous driving, virtual and augmented reality to device geo-localization. Recently, end-to-end learned localization approaches have been proposed which show promising results on small-scale datasets. However, the positioning accuracy, scalability, latency, and compute and storage requirements of these approaches remain open challenges. We aim to deploy localization at a global scale where one thus relies on methods using local features and sparse 3D models. Our approach spans from offline model building to real-time client-side pose fusion. The system compresses the appearance and geometry of the scene for efficient model storage and lookup leading to scalability beyond what has been demonstrated previously. It allows for low-latency localization queries and efficient fusion to be run in real-time on mobile platforms by combining server-side localization with real-time visual–inertial-based camera pose tracking. In order to further improve efficiency, we leverage a combination of priors, nearest-neighbor search, geometric match culling, and a cascaded pose candidate refinement step. This combination outperforms previous approaches when working with large-scale models and allows deployment at unprecedented scale. We demonstrate the effectiveness of our approach on a proof-of-concept system localizing 2.5 million images against models from four cities in different regions of the world achieving query latencies in the 200 ms range.
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Bald, Christin, and Gerhard Schmidt. "Processing Chain for Localization of Magnetoelectric Sensors in Real Time." Sensors 21, no. 16 (August 23, 2021): 5675. http://dx.doi.org/10.3390/s21165675.

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The knowledge of the exact position and orientation of a sensor with respect to a source (distribution) is essential for the correct solution of inverse problems. Especially when measuring with magnetic field sensors, the positions and orientations of the sensors are not always fixed during measurements. In this study, we present a processing chain for the localization of magnetic field sensors in real time. This includes preprocessing steps, such as equalizing and matched filtering, an iterative localization approach, and postprocessing steps for smoothing the localization outcomes over time. We show the efficiency of this localization pipeline using an exchange bias magnetoelectric sensor. For the proof of principle, the potential of the proposed algorithm performing the localization in the two-dimensional space is investigated. Nevertheless, the algorithm can be easily extended to the three-dimensional space. Using the proposed pipeline, we achieve average localization errors between 1.12 cm and 6.90 cm in a localization area of size 50cm×50cm.
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10

Han, Seung-Jun, and Jeongdan Choi. "Real-Time Precision Vehicle Localization Using Numerical Maps." ETRI Journal 36, no. 6 (December 1, 2014): 968–78. http://dx.doi.org/10.4218/etrij.14.0114.0040.

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11

Lim, Hyon, Sudipta N. Sinha, Michael F. Cohen, Matt Uyttendaele, and H. Jin Kim. "Real-time monocular image-based 6-DoF localization." International Journal of Robotics Research 34, no. 4-5 (March 25, 2015): 476–92. http://dx.doi.org/10.1177/0278364914561101.

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12

Piccinni, Giovanni, Gianfranco Avitabile, Giuseppe Coviello, and Claudio Talarico. "Real-Time Distance Evaluation System for Wireless Localization." IEEE Transactions on Circuits and Systems I: Regular Papers 67, no. 10 (October 2020): 3320–30. http://dx.doi.org/10.1109/tcsi.2020.2979347.

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13

Jayender, Jagadeesan, Thomas C. Lee, and Daniel T. Ruan. "Real-Time Localization of Parathyroid Adenoma during Parathyroidectomy." New England Journal of Medicine 373, no. 1 (July 2, 2015): 96–98. http://dx.doi.org/10.1056/nejmc1415448.

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14

Wiemhoefer, Martin, Daniel Boening, Julia Trahe, and Jürgen Klingauf. "Towards Real Time Analysis in Photoactivation Localization Microscopy." Biophysical Journal 98, no. 3 (January 2010): 679a. http://dx.doi.org/10.1016/j.bpj.2009.12.3730.

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15

Krapez, Peter, and Marko Munih. "Anchor Calibration for Real-Time-Measurement Localization Systems." IEEE Transactions on Instrumentation and Measurement 69, no. 12 (December 2020): 9907–17. http://dx.doi.org/10.1109/tim.2020.3005258.

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16

Malinowski, Kathleen T., Camille Noel, Meghana Roy, Twyla Willoughby, Toufik Djemi, Shirish Jani, Timothy Solberg, David Liu, Lisa Levine, and Parag J. Parikh. "Efficient use of continuous, real-time prostate localization." Physics in Medicine and Biology 53, no. 18 (August 18, 2008): 4959–70. http://dx.doi.org/10.1088/0031-9155/53/18/007.

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17

Bluemel, Christina, Paul Kirchner, Georg W. Kajdi, Rudolf A. Werner, and Ken Herrmann. "Localization of Parathyroid Adenoma With Real-Time Ultrasound." Clinical Nuclear Medicine 41, no. 3 (March 2016): e141-e142. http://dx.doi.org/10.1097/rlu.0000000000000960.

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18

Dinh, Christoph, Daniel Strohmeier, Martin Luessi, Daniel Güllmar, Daniel Baumgarten, Jens Haueisen, and Matti S. Hämäläinen. "Real-Time MEG Source Localization Using Regional Clustering." Brain Topography 28, no. 6 (March 18, 2015): 771–84. http://dx.doi.org/10.1007/s10548-015-0431-9.

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19

Jamtsho, Yonten, Panomkhawn Riyamongkol, and Rattapoom Waranusast. "Real-time Bhutanese license plate localization using YOLO." ICT Express 6, no. 2 (June 2020): 121–24. http://dx.doi.org/10.1016/j.icte.2019.11.001.

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20

Zhao, Yilu, Xiong Chen, and Bin Wang. "Real-time sound source localization using hybrid framework." Applied Acoustics 74, no. 12 (December 2013): 1367–73. http://dx.doi.org/10.1016/j.apacoust.2013.04.010.

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21

Zhao, Boxin, Xiaolong Chen, Xiaolin Zhao, Jun Jiang, and Jiahua Wei. "Real-Time UAV Autonomous Localization Based on Smartphone Sensors." Sensors 18, no. 12 (November 27, 2018): 4161. http://dx.doi.org/10.3390/s18124161.

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Localization in GPS-denied environments has become a bottleneck problem for small unmanned aerial vehicles (UAVs). Smartphones equipped with multi-sensors and multi-core processors provide a choice advantage for small UAVs for their high integration and light weight. However, the built-in phone sensor has low accuracy and the phone storage and computing resources are limited, which make the traditional localization methods unable to be readily converted to smartphone-based ones. The paper aims at exploring the feasibility of the phone sensors, and presenting a real-time, less memory autonomous localization method based on the phone sensors, so that the combination of “small UAV+smartphone” can operate in GPS-denied areas regardless of the overload problem. Indoor and outdoor flight experiments are carried out, respectively, based on an off-the-shelf smartphone and a XAircraft 650 quad-rotor platform. The results show that the precision performance of the phone sensors and real-time accurate localization in indoor environment is possible.
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22

Mohammed, Mohammed Ali, Dr Karim Q. Hussein, and Dr Mustafa Dhiaa Al-Hassani. "Real Time Mobile Cloud Audio Reading System for Blind Persons." Webology 19, no. 1 (January 20, 2022): 311–23. http://dx.doi.org/10.14704/web/v19i1/web19024.

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According to the World Health Organization there are approximately 285 million blind people around the world. These people are faced challenges when reading a book. This paper aims to design and implementation new real time mobile cloud audio reading system for blind persons. The proposed methodology consists of following steps: In Client Side, firstly, capture image of text by camera. Secondly, check the page localization. Thirdly, send image to server. In Cloud Side (Server Side), firstly, apply the modify EAST algorithm on received image to text detection. Secondly, apply OCR algorithm to extract text from image. Thirdly, apply post-processing step to correct the in corrected text. Finally, return text to client side to speak it using text-to-speech algorithm.
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23

He, Dongqing, Hsiu-Min Chuang, Jinyu Chen, Jinwei Li, and Akio Namiki. "Real-Time Visual Feedback Control of Multi-Camera UAV." Journal of Robotics and Mechatronics 33, no. 2 (April 20, 2021): 263–73. http://dx.doi.org/10.20965/jrm.2021.p0263.

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Recently, flight control of unmanned aerial vehicles (UAVs) in non-global positioning system (GPS) environments has become increasingly important. In such an environment, visual sensors are important, and their main roles are self-localization and obstacle avoidance. In this paper, the concept of a multi-camera UAV system with multiple cameras attached to the body is proposed to realize high-precision omnidirectional visual recognition, self-localization, and obstacle avoidance simultaneously, and a two-camera UAV is developed as a prototype. The proposed flight control system can switch between visual servoing (VS) for collision avoidance and visual odometry (VO) for self-localization. The feasibility of the proposed control system was verified by conducting flight experiments with the insertion of obstacles.
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24

Lee, Ji-Yeoun, Su-young Chi, Jae-Yeun Lee, Minsoo Hahn, and Young-Jo Cho. "REAL-TIME SOUND LOCALIZATION USING TIME DIFFERENCE FOR HUMAN ROBOT INTERACTION." IFAC Proceedings Volumes 38, no. 1 (2005): 54–57. http://dx.doi.org/10.3182/20050703-6-cz-1902.01411.

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25

Liu, M. H., Sheng Zhang, and Yi Pan. "UWB-based Real-Time 3D High Precision Localization System." Journal of Physics: Conference Series 2290, no. 1 (June 1, 2022): 012082. http://dx.doi.org/10.1088/1742-6596/2290/1/012082.

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Abstract Ultra-Wideband (UWB) positioning technique featuring high accuracy and low cost has been drawing increasing interest. Angel of arrival (AOA), time difference of arrival (TDOA) and time of arrival (TOA) are common methods for positioning. With DecaWave1000 ranging systems, which applies TOA method, sub-meter accuracy can be achieved easily. However, the traditional DW1000 UWB localization system is unscalable and the ranging stability remains improving. In this paper, a brand new Scalable Multi-Base Multi-Tag (SMBMT) scheme based on traditional DW1000 UWB localization system is proposed to improve the system’s efficiency and scalability. Then an effective data processing algorithm Data Kalman Filter is offered using ranging information only, to diminish the ranging error in the system. By changing the system equation, Data KF is also able to optimize the dynamic trajectory. Finally, the proposed system is tested and the algorithm is analysed. The result shows that not only does applying SMBMT scheme improves the scalability and ranging efficiency of UWB localization system but also increases the accuracy and stability in positioning.
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26

Sanda, Benjamin, Ikhlas Abdel-Qader, and Abiola Akanmu. "Reducing Tracking Error in RFID Real-Time Localization Systems Using Kalman Filters." International Journal of Handheld Computing Research 5, no. 3 (July 2014): 1–24. http://dx.doi.org/10.4018/ijhcr.2014070101.

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The use of Radio Frequency Identification (RFID) has become widespread in industry as a means to quickly and wirelessly identify and track packages and equipment. Now there is a commercial interest in using RFID to provide real-time localization. Efforts to use RFID technology in this way experience localization errors due to noise and multipath effects inherent to these environments. This paper presents the use of both linear Kalman filters and non-linear Unscented Kalman filters to reduce the error rate inherent to real-time RFID localization systems and provide more accurate localization results in indoor environments. A commercial RFID localization system designed for use by the construction industry is used in this work, and a filtering model based on 3rd order motion is developed. The filtering model is tested with real-world data and shown to provide an increase in localization accuracy when applied to both raw time of arrival measurements as well as final localization results.
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27

Ferrera, Maxime, Julien Moras, Pauline Trouvé-Peloux, and Vincent Creuze. "Real-Time Monocular Visual Odometry for Turbid and Dynamic Underwater Environments." Sensors 19, no. 3 (February 8, 2019): 687. http://dx.doi.org/10.3390/s19030687.

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In the context of underwater robotics, the visual degradation induced by the medium properties make difficult the exclusive use of cameras for localization purpose. Hence, many underwater localization methods are based on expensive navigation sensors associated with acoustic positioning. On the other hand, pure visual localization methods have shown great potential in underwater localization but the challenging conditions, such as the presence of turbidity and dynamism, remain complex to tackle. In this paper, we propose a new visual odometry method designed to be robust to these visual perturbations. The proposed algorithm has been assessed on both simulated and real underwater datasets and outperforms state-of-the-art terrestrial visual SLAM methods under many of the most challenging conditions. The main application of this work is the localization of Remotely Operated Vehicles used for underwater archaeological missions, but the developed system can be used in any other applications as long as visual information is available.
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28

Wu, Xiqian, Duy Hinh NGUYEN, Hideo AYUSAWA, Daisuke IWAKURA, and Kenzo NONAMI. "2A1-O02 Real-time Localization and Control with DP-SLAM(Localization and Mapping)." Proceedings of JSME annual Conference on Robotics and Mechatronics (Robomec) 2011 (2011): _2A1—O02_1—_2A1—O02_3. http://dx.doi.org/10.1299/jsmermd.2011._2a1-o02_1.

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29

Boyd, Michael C., Paul Steinbok, and Peter L. Cooperberg. "Intraoperative Localization of Intracranial Lesions with Real Time Ultrasound." Canadian Journal of Neurological Sciences / Journal Canadien des Sciences Neurologiques 12, no. 1 (February 1985): 31–34. http://dx.doi.org/10.1017/s0317167100046540.

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ABSTRACT:High resolution ultrasound has been used intraoperatively on forty-five patients with various intracranial lesions. The technique is quickly and easily carried out under sterile conditions in the operating room. Successful localization of both primary and metastatic tumors of various sizes, depths and consistencies have been made prior to extirpation or biopsy. Several of the biopsies were done through small burr holes. Arteriovenous malformations, abscesses, bone fragments from trauma, gliotic epileptic foci and ventricles for shunt placement have been readily found. No significant complications have been encountered. A new technique for localizing superficial lesions is described. An overall reduction in operating time and unnecessary trauma to the patient has resulted from more accurate intraoperative localization of intracranial lesions with real time ultrasound.
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30

Li, Rui-Qi, Xiao-Liang Xie, Xiao-Hu Zhou, Shi-Qi Liu, Zhen-Liang Ni, Yan-Jie Zhou, Gui-Bin Bian, and Zeng-Guang Hou. "Real-Time Multi-Guidewire Endpoint Localization in Fluoroscopy Images." IEEE Transactions on Medical Imaging 40, no. 8 (August 2021): 2002–14. http://dx.doi.org/10.1109/tmi.2021.3069998.

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31

Peyraud, Sébastien, Eric Royer, Stéphane Renault, and Dominique Meizel. "Collaborative Methods for Real-Time Localization in Urban Centers." International Journal of Advanced Robotic Systems 12, no. 11 (January 2015): 154. http://dx.doi.org/10.5772/61371.

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32

Wang, Xuan, Jinghong Liu, and Qianfei Zhou. "Real-Time Multi-Target Localization from Unmanned Aerial Vehicles." Sensors 17, no. 12 (December 25, 2016): 33. http://dx.doi.org/10.3390/s17010033.

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33

Belkin, Ilya, Alexander Abramenko, and Dmitry Yudin. "Real-Time Lidar-based Localization of Mobile Ground Robot." Procedia Computer Science 186 (2021): 440–48. http://dx.doi.org/10.1016/j.procs.2021.04.164.

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34

Yi, Yang, Yang Sun, Saimei Yuan, Yiji Zhu, Mengyi Zhang, and Wenjun Zhu. "COWO: towards real-time spatiotemporal action localization in videos." Assembly Automation 42, no. 2 (January 18, 2022): 202–8. http://dx.doi.org/10.1108/aa-07-2021-0098.

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Purpose The purpose of this paper is to provide a fast and accurate network for spatiotemporal action localization in videos. It detects human actions both in time and space simultaneously in real-time, which is applicable in real-world scenarios such as safety monitoring and collaborative assembly. Design/methodology/approach This paper design an end-to-end deep learning network called collaborator only watch once (COWO). COWO recognizes the ongoing human activities in real-time with enhanced accuracy. COWO inherits from the architecture of you only watch once (YOWO), known to be the best performing network for online action localization to date, but with three major structural modifications: COWO enhances the intraclass compactness and enlarges the interclass separability in the feature level. A new correlation channel fusion and attention mechanism are designed based on the Pearson correlation coefficient. Accordingly, a correction loss function is designed. This function minimizes the same class distance and enhances the intraclass compactness. Use a probabilistic K-means clustering technique for selecting the initial seed points. The idea behind this is that the initial distance between cluster centers should be as considerable as possible. CIOU regression loss function is applied instead of the Smooth L1 loss function to help the model converge stably. Findings COWO outperforms the original YOWO with improvements of frame mAP 3% and 2.1% at a speed of 35.12 fps. Compared with the two-stream, T-CNN, C3D, the improvement is about 5% and 14.5% when applied to J-HMDB-21, UCF101-24 and AGOT data sets. Originality/value COWO extends more flexibility for assembly scenarios as it perceives spatiotemporal human actions in real-time. It contributes to many real-world scenarios such as safety monitoring and collaborative assembly.
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35

Rameau, Francois, Oleksandr Bailo, Jinsun Park, Kyungdon Joo, and In So Kweon. "Real-Time Multi-Car Localization and See-Through System." International Journal of Computer Vision 130, no. 2 (January 4, 2022): 384–404. http://dx.doi.org/10.1007/s11263-021-01558-5.

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36

Ooshima, Naoya, Takeshi Saitoh, and Ryosuke Konishi. "Real-Time Person Localization System with an Active Camera." IEEJ Transactions on Electronics, Information and Systems 126, no. 8 (2006): 957–62. http://dx.doi.org/10.1541/ieejeiss.126.957.

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37

TANAKA, Kanji, and Eiji KONDO. "Towards Real-Time Global Localization in Dynamic Unstructured Environments." JSME International Journal Series C 49, no. 3 (2006): 905–11. http://dx.doi.org/10.1299/jsmec.49.905.

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38

Neumann, Lukas, and Jiri Matas. "Real-Time Lexicon-Free Scene Text Localization and Recognition." IEEE Transactions on Pattern Analysis and Machine Intelligence 38, no. 9 (September 1, 2016): 1872–85. http://dx.doi.org/10.1109/tpami.2015.2496234.

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39

Kulshrestha, Tarun, Divya Saxena, Rajdeep Niyogi, and Jiannong Cao. "Real-Time Crowd Monitoring Using Seamless Indoor-Outdoor Localization." IEEE Transactions on Mobile Computing 19, no. 3 (March 1, 2020): 664–79. http://dx.doi.org/10.1109/tmc.2019.2897561.

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40

Belloch, Jose A., Alberto Gonzalez, Antonio M. Vidal, and Maximo Cobos. "Real-time Sound Source Localization on Graphics Processing Units." Procedia Computer Science 18 (2013): 2549–52. http://dx.doi.org/10.1016/j.procs.2013.05.438.

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41

Kovacs, Anthony, Jonathan Kessler, Je-Luen Li, HuaWen Lin, Susan Dutcher, and Yan Mei Wang. "Real-Time Subcellular Localization Reveals Hidden Intraflagellar Transport Mechanisms." Biophysical Journal 112, no. 3 (February 2017): 313a. http://dx.doi.org/10.1016/j.bpj.2016.11.1695.

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42

Lewis, J. H., R. Li, W. T. Watkins, L. I. Cerviño, J. D. Lawson, W. Y. Song, and S. B. Jiang. "Real-time Localization of Lung Tumors during Arc Therapy." International Journal of Radiation Oncology*Biology*Physics 75, no. 3 (November 2009): S595. http://dx.doi.org/10.1016/j.ijrobp.2009.07.1360.

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43

Ejebe, G. C., R. F. Paliza, and W. F. Tinney. "An adaptive localization method for real-time security analysis." IEEE Transactions on Power Systems 7, no. 2 (May 1992): 777–83. http://dx.doi.org/10.1109/59.141785.

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44

Pan, Y. Y., X. Q. Ding, T. Yang, Y. Wang, R. Xiong, and L. Jiang. "Real-time localization, mapping and navigation for quadruped robots." Journal of Physics: Conference Series 1507 (April 2020): 052003. http://dx.doi.org/10.1088/1742-6596/1507/5/052003.

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45

Golbabaee, Mohammad, Alexandre Alahi, and Pierre Vandergheynst. "SCOOP: A Real-Time Sparsity Driven People Localization Algorithm." Journal of Mathematical Imaging and Vision 48, no. 1 (December 6, 2012): 160–75. http://dx.doi.org/10.1007/s10851-012-0405-4.

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46

MCGARY, J., and B. TEH. "Real-time prostate localization using superconducting quantum interference devices." International Journal of Radiation OncologyBiologyPhysics 60 (September 2004): S617. http://dx.doi.org/10.1016/s0360-3016(04)01930-3.

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47

Sangineto, Enver, and Marco Cupelli. "Real-time viewpoint-invariant hand localization with cluttered backgrounds." Image and Vision Computing 30, no. 1 (January 2012): 26–37. http://dx.doi.org/10.1016/j.imavis.2011.11.004.

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48

Suau, Xavier, Marcel Alcoverro, Adolfo López-Méndez, Javier Ruiz-Hidalgo, and Josep R. Casas. "Real-time fingertip localization conditioned on hand gesture classification." Image and Vision Computing 32, no. 8 (August 2014): 522–32. http://dx.doi.org/10.1016/j.imavis.2014.04.015.

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49

McGary, J. E., and B. S. Teh. "Real-time prostate localization using superconducting quantum interference devices." International Journal of Radiation Oncology*Biology*Physics 60, no. 1 (September 2004): S617. http://dx.doi.org/10.1016/j.ijrobp.2004.07.626.

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50

Mereghetti, S., D. Götz, and J. Borkowski. "Real time localization of gamma ray bursts with INTEGRAL." Advances in Space Research 34, no. 12 (January 2004): 2729–33. http://dx.doi.org/10.1016/j.asr.2003.02.074.

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