Väitös (tietotekniikka): MSc Qingqing Li
Aika
31.5.2024 klo 12.00 - 16.00
MSc Qingqing Li esittää väitöskirjansa ”LiDAR Based Multi-Sensor Fusion for Localization, Mapping, and Tracking” julkisesti tarkastettavaksi Turun yliopistossa perjantaina 31.05.2024 klo 12.00 (Turun yliopisto, Agora XXI, Turku).
Vastaväittäjänä toimii apulaisprofessori Evangelos Boukas (Technical University of Denmark, Tanska) ja kustoksena professori Tomi Westerlund (Turun yliopisto). Tilaisuus on englanninkielinen. Väitöksen alana on tietotekniikka.
Väitöskirja yliopiston julkaisuarkistossa: https://urn.fi/URN:ISBN:978-951-29-9736-7
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Tiivistelmä väitöstutkimuksesta:
This thesis explores LiDAR-based sensor fusion algorithms to address perception challenges in autonomous systems, focusing primarily on dense mapping and global localization using diverse LiDAR sensors. The research involves integrating novel LiDARs, IMUs, and camera sensors to create a comprehensive dataset essential for developing advanced sensor fusion and general-purpose localization and mapping algorithms. Innovative methodologies for global localization across varied environments are introduced, including a robust multi-modal LiDAR inertial odometry and a dense mapping framework, which enhance mapping precision and situational awareness. The study also integrates solid-state LiDARs with camera-based deep-learning techniques for object tracking, refining mapping accuracy in dynamic environments. These advancements significantly enhance the reliability and efficiency of autonomous systems in real-world scenarios.
Vastaväittäjänä toimii apulaisprofessori Evangelos Boukas (Technical University of Denmark, Tanska) ja kustoksena professori Tomi Westerlund (Turun yliopisto). Tilaisuus on englanninkielinen. Väitöksen alana on tietotekniikka.
Väitöskirja yliopiston julkaisuarkistossa: https://urn.fi/URN:ISBN:978-951-29-9736-7
***
Tiivistelmä väitöstutkimuksesta:
This thesis explores LiDAR-based sensor fusion algorithms to address perception challenges in autonomous systems, focusing primarily on dense mapping and global localization using diverse LiDAR sensors. The research involves integrating novel LiDARs, IMUs, and camera sensors to create a comprehensive dataset essential for developing advanced sensor fusion and general-purpose localization and mapping algorithms. Innovative methodologies for global localization across varied environments are introduced, including a robust multi-modal LiDAR inertial odometry and a dense mapping framework, which enhance mapping precision and situational awareness. The study also integrates solid-state LiDARs with camera-based deep-learning techniques for object tracking, refining mapping accuracy in dynamic environments. These advancements significantly enhance the reliability and efficiency of autonomous systems in real-world scenarios.
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