Relevant bibliographies by topics / Sensors fusion for localisation

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Sensors fusion for localisation'

Author: Grafiati
Published: 25 May 2024
Last updated: 31 July 2025

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Journal articles on the topic "Sensors fusion for localisation"

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Zheng, ShiYu, and Xiang Jing Yu. "An Overview Study of Multi-sensor Fusion for Map Building and Localization." Advances in Research 26, no. 1 (2025): 247–56. https://doi.org/10.9734/air/2025/v26i11250.

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This paper reviews the application of multi-sensor fusion in simultaneous localisation and map construction (SLAM) technology. With the development of robotics, autonomous driving and virtual reality, there is an increasing demand for precise localisation and map construction.SLAM technology has emerged to solve the problem of autonomous robot localisation and map construction in unknown environments. However, single-sensor SLAM systems have limitations, such as limited sensing capability and susceptibility to noise interference. Multi-sensor fusion SLAM significantly improves the performance
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Ashokaraj, Immanuel, Antonios Tsourdos, Peter Silson, and Brian White. "SENSOR BASED ROBOT LOCALISATION AND NAVIGATION: USING INTERVAL ANALYSIS AND NONLINEAR KALMAN FILTERS." Transactions of the Canadian Society for Mechanical Engineering 29, no. 2 (2005): 211–27. http://dx.doi.org/10.1139/tcsme-2005-0014.

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Multiple sensor fusion for robot localisation and navigation has attracted a lot of interest in recent years. This paper describes a sensor based navigation and localisation approach for an autonomous mobile robot using an interval analysis (IA) based adaptive mechanism for the non-linear Kalman filter namely the Extended Kalman filter (EKF). The map used for this study is two-dimensional and assumed to be known a priori. The robot is equipped with inertial sensors (INS), encoders and ultrasonic sensors. A non-linear Kalman filter is used to estimate the robots position using the inertial sens
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Meng, Lijun, Zhengang Guo, and Chenglong Ma. "Research on multiple damage localisation based on fusion of the Lamb wave ellipse algorithm and RAPID algorithm." Insight - Non-Destructive Testing and Condition Monitoring 66, no. 1 (2024): 34–40. http://dx.doi.org/10.1784/insi.2024.66.1.34.

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Current damage localisation methods often require many sensors and complex signal processing methods. This paper proposes a fusion algorithm based on elliptical localisation and the reconstruction algorithm for probabilistic inspection of damage (RAPID) to locate and image multiple damages. Experimental verification of the damage algorithm was conducted. An ultrasonic probe was used to excite Lamb signals on an aluminium alloy plate, the ultrasonic response signals at different positions within the plate under multiple damages were measured and the constructed algorithm was employed to image t
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Nikitenko, Agris, Aleksis Liekna, Martins Ekmanis, Guntis Kulikovskis, and Ilze Andersone. "Single Robot Localisation Approach for Indoor Robotic Systems through Integration of Odometry and Artificial Landmarks." Applied Computer Systems 14, no. 1 (2013): 50–58. http://dx.doi.org/10.2478/acss-2013-0006.

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Abstract we present an integrated approach for robot localization that allows to integrate for the artificial landmark localization data with odometric sensors and signal transfer function data to provide means for different practical application scenarios. The sensor data fusion deals with asynchronous sensor data using inverse Laplace transform. We demonstrate a simulation software system that ensures smooth integration of the odometry-based and signal transfer - based localization into one approach.
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Tibebu, Haileleol, Varuna De-Silva, Corentin Artaud, Rafael Pina, and Xiyu Shi. "Towards Interpretable Camera and LiDAR Data Fusion for Autonomous Ground Vehicles Localisation." Sensors 22, no. 20 (2022): 8021. http://dx.doi.org/10.3390/s22208021.

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Recent deep learning frameworks draw strong research interest in application of ego-motion estimation as they demonstrate a superior result compared to geometric approaches. However, due to the lack of multimodal datasets, most of these studies primarily focused on single-sensor-based estimation. To overcome this challenge, we collect a unique multimodal dataset named LboroAV2 using multiple sensors, including camera, light detecting and ranging (LiDAR), ultrasound, e-compass and rotary encoder. We also propose an end-to-end deep learning architecture for fusion of RGB images and LiDAR laser s
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Donati, Cesare, Martina Mammarella, Lorenzo Comba, Alessandro Biglia, Paolo Gay, and Fabrizio Dabbene. "3D Distance Filter for the Autonomous Navigation of UAVs in Agricultural Scenarios." Remote Sensing 14, no. 6 (2022): 1374. http://dx.doi.org/10.3390/rs14061374.

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In precision agriculture, remote sensing is an essential phase in assessing crop status and variability when considering both the spatial and the temporal dimensions. To this aim, the use of unmanned aerial vehicles (UAVs) is growing in popularity, allowing for the autonomous performance of a variety of in-field tasks which are not limited to scouting or monitoring. To enable autonomous navigation, however, a crucial capability lies in accurately locating the vehicle within the surrounding environment. This task becomes challenging in agricultural scenarios where the crops and/or the adopted t
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Moretti, Michele, Federico Bianchi, and Nicola Senin. "Towards the development of a smart fused filament fabrication system using multi-sensor data fusion for in-process monitoring." Rapid Prototyping Journal 26, no. 7 (2020): 1249–61. http://dx.doi.org/10.1108/rpj-06-2019-0167.

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Purpose This paper aims to illustrate the integration of multiple heterogeneous sensors into a fused filament fabrication (FFF) system and the implementation of multi-sensor data fusion technologies to support the development of a “smart” machine capable of monitoring the manufacturing process and part quality as it is being built. Design/methodology/approach Starting from off-the-shelf FFF components, the paper discusses the issues related to how the machine architecture and the FFF process itself must be redesigned to accommodate heterogeneous sensors and how data from such sensors can be in
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Kozłowski, Michał, Raúl Santos-Rodríguez, and Robert Piechocki. "Sensor Modalities and Fusion for Robust Indoor Localisation." ICST Transactions on Ambient Systems 6, no. 18 (2019): 162670. http://dx.doi.org/10.4108/eai.12-12-2019.162670.

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Merfels, Christian, and Cyrill Stachniss. "Sensor Fusion for Self-Localisation of Automated Vehicles." PFG – Journal of Photogrammetry, Remote Sensing and Geoinformation Science 85, no. 2 (2017): 113–26. http://dx.doi.org/10.1007/s41064-017-0008-1.

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Ciuffreda, Ilaria, Sara Casaccia, and Gian Marco Revel. "A Multi-Sensor Fusion Approach Based on PIR and Ultrasonic Sensors Installed on a Robot to Localise People in Indoor Environments." Sensors 23, no. 15 (2023): 6963. http://dx.doi.org/10.3390/s23156963.

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This work illustrates an innovative localisation sensor network that uses multiple PIR and ultrasonic sensors installed on a mobile social robot to localise occupants in indoor environments. The system presented aims to measure movement direction and distance to reconstruct the movement of a person in an indoor environment by using sensor activation strategies and data processing techniques. The data collected are then analysed using both a supervised (Decision Tree) and an unsupervised (K-Means) machine learning algorithm to extract the direction and distance of occupant movement from the mea
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Dissertations / Theses on the topic "Sensors fusion for localisation"

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Millikin, R. L. "Sensor fusion for the localisation of birds in flight." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2002. http://www.collectionscanada.ca/obj/s4/f2/dsk3/ftp04/NQ65871.pdf.

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Welte, Anthony. "Spatio-temporal data fusion for intelligent vehicle localization." Thesis, Compiègne, 2020. http://bibliotheque.utc.fr/EXPLOITATION/doc/IFD/2020COMP2572.

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La localisation précise constitue une brique essentielle permettant aux véhicules de naviguer de manière autonome sur la route. Cela peut être atteint à travers les capteurs déjà existants, de nouvelles technologies (Iidars, caméras intelligentes) et des cartes haute définition. Dans ce travail, l'intérêt d'enregistrer et réutiliser des informations sauvegardées en mémoire est exploré. Les systèmes de localisation doivent permettre une estimation à haute fréquence, des associations de données, de la calibration et de la détection d'erreurs. Une architecture composée de plusieurs couches de tra
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Lilja, Robin. "A Localisation and Navigation System for an Autonomous Wheel Loader." Thesis, Mälardalens högskola, Akademin för innovation, design och teknik, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-12157.

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Autonomous vehicles are an emerging trend in robotics, seen in a vast range of applications and environments. Consequently, Volvo Construction Equipment endeavour to apply the concept of autonomous vehicles onto one of their main products. In the company’s Autonomous Machine project an autonomous wheel loader is being developed. As an ob jective given by the company; a demonstration proving the possibility of conducting a fully autonomous load and haul cycle should be performed. Conducting such cycle requires the vehicle to be able to localise itself in its task space and navigate accordingly.
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Matsumoto, Takeshi, and takeshi matsumoto@flinders edu au. "Real-Time Multi-Sensor Localisation and Mapping Algorithms for Mobile Robots." Flinders University. Computer Science, Engineering and Mathematics, 2010. http://catalogue.flinders.edu.au./local/adt/public/adt-SFU20100302.131127.

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A mobile robot system provides a grounded platform for a wide variety of interactive systems to be developed and deployed. The mobility provided by the robot presents unique challenges as it must observe the state of the surroundings while observing the state of itself with respect to the environment. The scope of the discipline includes the mechanical and hardware issues, which limit and direct the capabilities of the software considerations. The systems that are integrated into the mobile robot platform include both specific task oriented and fundamental modules that define the core behaviou
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Khairallah, Mahmoud. "Flow-Based Visual-Inertial Odometry for Neuromorphic Vision Sensors." Electronic Thesis or Diss., université Paris-Saclay, 2022. http://www.theses.fr/2022UPAST117.

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Plutôt que de générer des images de manière constante et synchrone, les capteurs neuromorphiques de vision -également connus sous le nom de caméras événementielles, permettent à chaque pixel de fournir des informations de manière indépendante et asynchrone chaque fois qu'un changement de luminosité est détecté. Par conséquent, les capteurs de vision neuromorphiques n'ont pas les problèmes des caméras conventionnelles telles que les artefacts d'image et le Flou cinétique. De plus, ils peuvent fournir une compression sans perte de donné avec une résolution temporelle et une plage dynamique plus
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Salehi, Achkan. "Localisation précise d'un véhicule par couplage vision/capteurs embarqués/systèmes d'informations géographiques." Thesis, Université Clermont Auvergne‎ (2017-2020), 2018. http://www.theses.fr/2018CLFAC064/document.

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La fusion entre un ensemble de capteurs et de bases de données dont les erreurs sont indépendantes est aujourd’hui la solution la plus fiable et donc la plus répandue de l’état de l’art au problème de la localisation. Les véhicules semi-autonomes et autonomes actuels, ainsi que les applications de réalité augmentée visant les contextes industriels exploitent des graphes de capteurs et de bases de données de tailles considérables, dont la conception, la calibration et la synchronisation n’est, en plus d’être onéreuse, pas triviale. Il est donc important afin de pouvoir démocratiser ces technolo
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Héry, Elwan. "Localisation coopérative de véhicules autonomes communicants." Thesis, Compiègne, 2019. http://www.theses.fr/2019COMP2516.

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Afin de naviguer en autonomie un véhicule doit être capable de se localiser précisément par rapport aux bords de voie pour ne pas sortir de celle-ci et par rapport aux véhicules et piétons pour ne pas causer d'accident. Cette thèse traite de l'intérêt de la communication dans l'amélioration de la localisation des véhicules autonomes. La navigation autonome sur route est souvent réalisée à partir de coordonnées cartésiennes. Afin de mieux représenter la pose d'un véhicule relativement à la voie dans laquelle il circule, nous étudions l'utilisation de coordonnées curvilignes le long de chemins e
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Jacobson, Adam. "Bio-inspired multi-sensor fusion and calibration for robot place learning and recognition." Thesis, Queensland University of Technology, 2018. https://eprints.qut.edu.au/116179/1/Adam%20Jacobson%20Thesis.pdf.

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Determining an agent's location in the world is vital for robotic navigation, path planning and co-operative behaviours. This thesis focuses on the translation of biological insights to the robotics domain to improve topological SLAM with an aim to enable robot navigation and localisation without human intervention. The primary contributions presented within this thesis are SLAM localisation techniques which are robust to environmental changes, require minimal or no human intervention for setup within a new environment and are robust to sensor failures.
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Ericsson, John-Eric, and Daniel Eriksson. "Indoor Positioning and Localisation System with Sensor Fusion : AN IMPLEMENTATION ON AN INDOOR AUTONOMOUS ROBOT AT ÅF." Thesis, KTH, Maskinkonstruktion (Inst.), 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-168841.

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This thesis will present guidelines of how to select sensors and algorithms for indoor positioning and localisation systems with sensor fusion. These guidelines are based on an extensive theory and state of the art research. Different scenarios are presented to give some examples of proposed sensors and algorithms for certain applications. There are of course no right or wrong sensor combinations, but some factors are good to bear in mind when a system is designed. To give an example of the proposed guidelines a Simultaneous Localisation and Mapping (SLAM) system as well as an Indoor Positioni
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Ladhari, Maroua. "Architecture générique de fusion par approche Top-Down : application à la localisation d’un robot mobile." Thesis, Université Clermont Auvergne‎ (2017-2020), 2020. http://www.theses.fr/2020CLFAC052.

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La problématique qui va être abordée dans cette thèse est la localisation d’un robot mobile. Ce dernier, équipé de capteurs bas-coût, cherche à exploiter le maximum d’informations possibles pour répondre à un objectif fixé au préalable. Un problème de fusion de données sera traité d’une manière à ce qu’à chaque situation, le robot saura quelle information utiliser pour se localiser d’une manière continue. Les données que nous allons traiter seront de différents types. Dans nos travaux, deux propriétés de localisation sont désirées: la précision et la confiance. Pour pouvoir le contrôler, le ro
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Books on the topic "Sensors fusion for localisation"

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G, Buser Rudolph, Warren Frank B, Society of Photo-optical Instrumentation Engineers., and University of Alabama in Huntsville. Center for Applied Optics., eds. Infrared sensors and sensor fusion: 19-21 May, 1987, Orlando, Florida. SPIE--the International Society for Optical Engineering, 1987.

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Hucks, John A. Fusion of ground-based sensors for optimal tracking of military targets. Naval Postgraduate School, 1989.

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Otmar, Loffeld, Centre national de la recherche scientifique (France), and Society of Photo-optical Instrumentation Engineers., eds. Vision systems--sensors, sensor systems, and components: 10-12 June 1996, Besançon, France. SPIE--the International Society for Optical Engineering, 1996.

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Otmar, Loffeld, Society of Photo-optical Instrumentation Engineers., European Optical Society, and Commission of the European Communities. Directorate-General for Science, Research, and Development., eds. Sensors, sensor systems, and sensor data processing: June 16-17 1997, Munich, FRG. SPIE, 1997.

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D, Lara Yejas Oscar, ed. Human activity recognition: Using wearable sensors and smartphones. Taylor & Francis, 2013.

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Intelligent Sensors, Sensor Networks & Information Processing Conference (2nd 2005 Melbourne, Vic.). Proceedings of the 2005 Intelligent Sensors, Sensor Networks & Information Processing Conference: 5-8 December, 2005, Melbourne, Australia. IEEE, 2006.

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Intelligent Sensors, Sensor Networks & Information Processing Conference (2nd 2005 Melbourne, Vic.). Proceedings of the 2005 Intelligent Sensors, Sensor Networks & Information Processing Conference: 5-8 December, 2005, Melbourne, Australia. IEEE, 2006.

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IEEE/AESS Dayton Chapter Symposium (15th 1998 Fairborn, OH). Sensing the world: Analog sensors & systems across the spectrum : the 15th Annual AESS/IEEE Dayton Section Symposium, Fairborn, OH, 14-15 May 1998. Institute of Electrical and Electronics Engineers, 1998.

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Green, Milford B. Mergers and acquisitions: Geographical and spatial perspectives. Routledge, 1990.

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Iniewski, Krzysztof, and Kevin Yallup. Technologies for Smart Sensors and Sensor Fusion. Taylor & Francis Group, 2017.

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Book chapters on the topic "Sensors fusion for localisation"

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Espinosa, Jose, Mihalis Tsiakkas, Dehao Wu, et al. "A Hybrid Underwater Acoustic and RF Localisation System for Enclosed Environments Using Sensor Fusion." In Towards Autonomous Robotic Systems. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-96728-8_31.

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Mitchell, H. B. "Image Sensors." In Image Fusion. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-11216-4_2.

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Kiaer, Jieun. "Fusion, localisation, and hybridity." In Delicious Words. Routledge, 2020. http://dx.doi.org/10.4324/9780429321801-4.

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Majumder, Bansari Deb, and Joyanta Kumar Roy. "Multifunction Data Fusion." In Multifunctional Sensors. CRC Press, 2023. http://dx.doi.org/10.1201/9781003350484-4.

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Mitchell, H. B. "Sensors." In Data Fusion: Concepts and Ideas. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-27222-6_2.

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Koch, Wolfgang. "Characterizing Objects and Sensors." In Tracking and Sensor Data Fusion. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-39271-9_2.

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Subramanian, Rajesh. "Additional Sensors and Sensor Fusion." In Build Autonomous Mobile Robot from Scratch using ROS. Apress, 2023. http://dx.doi.org/10.1007/978-1-4842-9645-5_9.

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Suciu, George, Andrei Scheianu, Cristina Mihaela Bălăceanu, et al. "Sensors Fusion Approach Using UAVs and Body Sensors." In Advances in Intelligent Systems and Computing. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-77700-9_15.

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Raol, Jitendra R., S. Sethu Selvi, Sudesh K. Kashyap, and Ailneni Sanketh. "Multi-Target Angle Only Tracking Using Thermal Imaging Sensors." In Data Fusion Mathematics, 2nd ed. CRC Press, 2025. https://doi.org/10.1201/9781003560265-13.

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Věchet, S., and J. Krejsa. "Sensors Data Fusion via Bayesian Network." In Recent Advances in Mechatronics. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-05022-0_38.

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Conference papers on the topic "Sensors fusion for localisation"

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Cozens, James M., and Simon J. Godsill. "Bimodal Multi-Object Localisation, Siteswap Inference, and Analysis for Competitive Juggling." In 2024 27th International Conference on Information Fusion (FUSION). IEEE, 2024. http://dx.doi.org/10.23919/fusion59988.2024.10706333.

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Badillo, Diego, and Marcelo A. Soto. "Acoustic Source Localisation Based on Distributed Acoustic Sensing and Sequential Least Squares Programming." In Optical Sensors. Optica Publishing Group, 2024. https://doi.org/10.1364/sensors.2024.sf4c.4.

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A method based on beamforming and sequential least squares programming is proposed for acoustic source localisation using fibre-optic distributed acoustic sensors. The method is experimentally validated and compared with another state-of-the-art approach.
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Jarvis, R. A. "Autonomous Robot Localisation By Sensor Fusion." In IEEE International Workshop on Emerging Technologies and Factory Automation,. IEEE, 1992. http://dx.doi.org/10.1109/etfa.1992.683295.

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Izri, Sonia, and Eric Brassart. "Uncertainties quantification criteria for multi-sensors fusion: Application to vehicles localisation." In Automation (MED 2008). IEEE, 2008. http://dx.doi.org/10.1109/med.2008.4602171.

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Redzic, Milan, Conor Brennan, and Noel E. O'Connor. "Dual-sensor fusion for indoor user localisation." In the 19th ACM international conference. ACM Press, 2011. http://dx.doi.org/10.1145/2072298.2071948.

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Franken, Dietrich. "An approximate maximum-likelihood estimator for localisation using bistatic measurements." In 2018 Sensor Data Fusion: Trends, Solutions, Applications (SDF). IEEE, 2018. http://dx.doi.org/10.1109/sdf.2018.8547074.

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Alvarado, Biel Piero, Fernando Matia, and Ramon Galan. "Improving indoor robots localisation by fusing different sensors." In 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2018. http://dx.doi.org/10.1109/iros.2018.8593667.

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Ristic, Branko, Mark Morelande, Alfonso Farina, and S. Dulman. "On Proximity-Based Range-Free Node Localisation in Wireless Sensor Networks." In 2006 9th International Conference on Information Fusion. IEEE, 2006. http://dx.doi.org/10.1109/icif.2006.301734.

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Corral-Plaza, David, Olaf Reich, Erik Hübner, Matthias Wagner, and Inmaculada Medina-Bulo. "A SENSOR FUSION SYSTEM IDENTIFYING COMPLEX EVENTS FOR LOCALISATION ESTIMATION." In International Conference on Applied Computing 2019. IADIS Press, 2019. http://dx.doi.org/10.33965/ac2019_201912c033.

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Khoder, Makkawi, Ait-Tmazirte Nourdine, El Badaoui El Najjar Maan та Moubayed Nazih. "Fault Tolerant multi-sensor Data Fusion for vehicle localisation using Maximum Correntropy Unscented Information Filter and α-Rényi Divergence". У 2020 IEEE 23rd International Conference on Information Fusion (FUSION). IEEE, 2020. http://dx.doi.org/10.23919/fusion45008.2020.9190407.

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Reports on the topic "Sensors fusion for localisation"

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Cadwallader, L. C. Reliability estimates for selected sensors in fusion applications. Office of Scientific and Technical Information (OSTI), 1996. http://dx.doi.org/10.2172/425367.

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Lane, Brandon, Lars Jacquemetton, Martin Piltch, and Darren Beckett. Thermal calibration of commercial melt pool monitoring sensors on a laser powder bed fusion system. National Institute of Standards and Technology, 2020. http://dx.doi.org/10.6028/nist.ams.100-35.

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Beiker, Sven. Next-generation Sensors for Automated Road Vehicles. SAE International, 2023. http://dx.doi.org/10.4271/epr2023003.

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<div class="section abstract"><div class="htmlview paragraph">This follow-up report to the inaugural SAE EDGE Research Report on “Unsettled Topics Concerning Sensors for Automated Road Vehicles” reviews the progress made in automated vehicle (AV) sensors over the past four to five years. Additionally, it addresses persistent disagreement and confusion regarding certain terms for describing sensors, the different strengths and shortcomings of particular sensors, and procedures regarding how to specify and evaluate them.</div><div class="htmlview paragraph"><b>Next-
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Mobley, Curtis D. Determining the Scattering Properties of Vertically-Structured Nepheloid Layers from the Fusion of Active and Passive Optical Sensors. Defense Technical Information Center, 2006. http://dx.doi.org/10.21236/ada630921.

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Guo, Ge, Jiageng Liu, and Guangheng Liu. Multi-sensor Fusion-based Vehicle Localization. SAE International, 2024. http://dx.doi.org/10.4271/epr2024023.

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<div class="section abstract"><div class="htmlview paragraph">Multi-sensor fusion (MSF) is believed to be a promising tool for vehicular localization in urban environments. Due to the differences in principles and performance of various onboard vehicle sensors, MSF inevitably suffers from heterogeneous sources and vulnerability to cyber-attacks. Therefore, an essential requirement of MSF is the capability of providing a consumer-grade solution that operates in real-time, is accurate, and immune to abnormal conditions with guaranteed performance and quality of service for location-b
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Kulhandjian, Hovannes. Detecting Driver Drowsiness with Multi-Sensor Data Fusion Combined with Machine Learning. Mineta Transportation Institute, 2021. http://dx.doi.org/10.31979/mti.2021.2015.

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In this research work, we develop a drowsy driver detection system through the application of visual and radar sensors combined with machine learning. The system concept was derived from the desire to achieve a high level of driver safety through the prevention of potentially fatal accidents involving drowsy drivers. According to the National Highway Traffic Safety Administration, drowsy driving resulted in 50,000 injuries across 91,000 police-reported accidents, and a death toll of nearly 800 in 2017. The objective of this research work is to provide a working prototype of Advanced Driver Ass
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Kulhandjian, Hovannes. AI-based Pedestrian Detection and Avoidance at Night using an IR Camera, Radar, and a Video Camera. Mineta Transportation Institute, 2022. http://dx.doi.org/10.31979/mti.2022.2127.

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Abstract:
In 2019, the United States experienced more than 6,500 pedestrian fatalities involving motor vehicles which resulted in a 67% rise in nighttime pedestrian fatalities and only a 10% rise in daytime pedestrian fatalities. In an effort to reduce fatalities, this research developed a pedestrian detection and alert system through the application of a visual camera, infrared camera, and radar sensors combined with machine learning. The research team designed the system concept to achieve a high level of accuracy in pedestrian detection and avoidance during both the day and at night to avoid potentia
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