Hriday Bavle
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| Department | Automation | ||||
| Adresse postale |
Université du Luxembourg 29, avenue JF Kennedy L-1855 Luxembourg |
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| Bureau sur le campus | JFK Building, E04-424 | ||||
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| Téléphone | (+352) 46 66 44 9954 | ||||
Dr. Hriday Bavle received his PhD degree from the Computer Vision and Aerial Robotics Group, Universidad Politécnica de Madrid, Spain. During his PhD he researched towards developing algorithms for improving the robustness of the localization and mapping systems on-board the aerial robots mainly using Computer Vision based techniques. He has published research in several well known and high impact conferences and journals.
He works as a Post Doctoral Research Associate at the Automation and Robotics Research group headed by Prof. Holger Voos and researches on Multi-Model Situation Awareness for mobile robots. He also holds an experience in the robotics industry, working as a Navigation and Perception Robotics Engineer at Blue Ocean Robotics, Denmark.
Links -
Last updated on: vendredi 01 octobre 2021
2022
Unclonable human-invisible machine vision markers leveraging the omnidirectional chiral Bragg diffraction of cholesteric spherical reflectors; ; ; ; ; ; ; ; ; ; ;
in Light: Science and Applications (2022), 11(309), 10103841377-022-01002-4
S-Graphs+: Real-time Localization and Mapping leveraging Hierarchical Representations; ; ; ;
E-print/Working paper (2022)
Advanced Situational Graphs for Robot Navigation in Structured Indoor Environments; ; ; ;
E-print/Working paper (2022)
Situational Graphs for Robot Navigation in Structured Indoor Environments; ; ; ;
E-print/Working paper (2022)
A Review of Radio Frequency Based Localisation for Aerial and Ground Robots with 5G Future Perspectives; ; ; ;
in Sensors (2022), 23(1), 188
2021
2020
Building the executive system of autonomous aerial robots using the Aerostack open-source framework; ; ; ; ;
in International Journal of Advanced Robotic Systems (2020), 17(3), 1--20
2019
An execution control method for the Aerostack aerial robotics framework; ; ; ;
in Frontiers of Information Technology and Electronic Engineering (2019), 20(1), 60--75
Vision-based multirotor following using synthetic learning techniques; ; ; ;
in Sensors (2019), 19(21), 1--20
A Fully-Autonomous Aerial Robot for Search and Rescue Applications in Indoor Environments using Learning-Based Techniques; ; ; ; ;
in Journal of Intelligent and Robotic Systems (2019), 95(2), 601--627
2018
Stereo Visual Odometry and Semantics based Localization of Aerial Robots in Indoor Environments; ; ; ; ;
Scientific Conference (2018)
Fast and Robust Flight Altitude Estimation of Multirotor UAVs in Dynamic Unstructured Environments Using 3D Point Cloud Sensors; ; ; ; ;
in Aerospace (2018), 5(3),
Attitude estimation using horizon detection in thermal images; ;
in International Journal of Micro Air Vehicles (2018), 10(4), 352--361
A Deep Reinforcement Learning Technique for Vision-Based Autonomous Multirotor Landing on a Moving Platform; ; ; ;
in IEEE International Conference on Intelligent Robots and Systems (2018)
Laser-Based Reactive Navigation for Multirotor Aerial Robots using Deep Reinforcement Learning; ; ; ;
Scientific Conference (2018)
2017
A flight altitude estimator for multirotor UAVs in dynamic and unstructured indoor environments; ; ; ;
in 2017 International Conference on Unmanned Aircraft Systems (ICUAS) (2017, June)
Towards fully autonomous landing on moving platforms for rotary Unmanned Aerial Vehicles; ; ; ; ;
Scientific Conference (2017)
A fully-autonomous aerial robotic solution for the 2016 International Micro Air Vehicle competition; ; ; ; ; ;
in 2017 International Conference on Unmanned Aircraft Systems (ICUAS) (2017, June)
A Multi-Layered Component-Based Approach for the Development of Aerial Robotic Systems: The Aerostack Framework; ; ; ; ; ; ;
in Journal of Intelligent and Robotic Systems (2017), 88(2), 638-709
