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Word cloudA word cloud is a visual representation of the most frequently used words in a text or a set of texts. The words appear in different sizes, with the size of each word being proportional to its frequency of occurrence in the text. The more frequently a word is used, the larger it appears in the word cloud. This technique allows for a quick visualization of the most important themes and concepts in a text.
In the context of this page, the word cloud was generated from the publications of the author {}. The words in this cloud come from the titles, abstracts, and keywords of the author's articles and research papers. By analyzing this word cloud, you can get an overview of the most recurring and significant topics and research areas in the author's work.
The word cloud is a useful tool for identifying trends and main themes in a corpus of texts, thus facilitating the understanding and analysis of content in a visual and intuitive way.
Ahmed, S. S., Shalaby, M., Le Ny, J., & Forbes, J. R. (2024, October). Optimal Robot Formations: Balancing Range-Based Observability and User-Defined Configurations [Paper]. IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2021), Abu Dhabi, United Arab Emirates. Published in 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). External link
Cossette, C. C., Shalaby, M. A., Saussié, D., & Forbes, J. R. (2024). Decentralized state estimation: An approach using pseudomeasurements and preintegration. The international journal of robotics research, 21 pages. Available
Cossette, C. C., Shalaby, M. A., Saussié, D., Le Ny, J., & Forbes, J. R. (2022, October). Optimal Multi-robot Formations for Relative Pose Estimation Using Range Measurements [Paper]. IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2022), Kyoto, Japan. External link
Cossette, C. C., Shalaby, M., Saussié, D., & Forbes, J. R. (2021, September). Localization with Directional Coordinates [Paper]. IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2021), Prague, Czech Republic. External link
Cossette, C. C., Shalaby, M., Saussié, D., Forbes, J. R., & Le Ny, J. (2021). Relative Position Estimation Between Two UWB Devices with IMUs. IEEE Robotics and Automation Letters, 6(3), 4313-4320. External link
Champagne Cossette, C., Forbes, J. R., & Saussié, D. (2020). Lagrangian Derivation of Variable-Mass Equations of Motion using an Arbitrary Attitude Parameterization. Journal of the Astronautical Sciences, 67(4), 1206-1219. Available
Shalaby, M. A., Cossette, C. C., Le Ny, J., & Forbes, J. R. (2024). Multi-Robot Relative Pose Estimation and IMU Preintegration Using Passive UWB Transceivers. IEEE Transactions on Robotics, 3370027 (20 pages). External link
Shalaby, M. A., Cossette, C. C., Forbes, J. R., & Le Ny, J. (2024). Reducing two-Way ranging variance by signal-timing optimization. IEEE Transactions on Aerospace and Electronic Systems, 30(3), 3718-3724. External link
Shalaby, M. A., Cossette, C. C., Forbes, J. R., & Le Ny, J. (2023, May). Calibration and Uncertainty Characterization for Ultra-Wideband Two-Way-Ranging Measurements [Paper]. 2023 IEEE International Conference on Robotics and Automation (ICRA 2023), London, UK. External link
Shalaby, M., Cossette, C. C., Le Ny, J., & Forbes, J. R. (2021). Cascaded Filtering Using the Sigma Point Transformation. IEEE Robotics and Automation Letters, 6(3), 4758-4765. External link
Shalaby, M., Cossette, C. C., Forbes, J. R., & Le Ny, J. (2021). Relative Position Estimation in Multi-Agent Systems Using Attitude-Coupled Range Measurements. IEEE Robotics and Automation Letters, 6(3), 4955-4961. External link
