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|Title:||MULTI-SENSOR STATE ESTIMATION FOR MICRO AERIAL VEHICLES IN COMPLEX ENVIRONMENTS||Authors:||BI YINGCAI||Keywords:||MAV, state estimation, multi-sensor fusion, Kalman filter, SLAM, visual-inertial odometry||Issue Date:||24-Aug-2018||Citation:||BI YINGCAI (2018-08-24). MULTI-SENSOR STATE ESTIMATION FOR MICRO AERIAL VEHICLES IN COMPLEX ENVIRONMENTS. ScholarBank@NUS Repository.||Abstract:||This thesis studies the state estimation methods for Micro Aerial Vehicles (MAVs) in complex environments. Robust state estimation is the frontier to enable full autonomy in a variety of applications. For the full state estimation in typical indoor/outdoor scenarios, we develop a laser-based multi-sensor fusion method using Kalman filter. For general 3D environments, we propose a hybrid approach for stereo-inertial sensors leveraging the advantages of both feature-based tracking and visual-inertial fusion. For GPS-denied state estimation at night, we design a visual odometry pipeline with a thermal camera and a single beam distance sensor. For state estimation of multiple MAVs in indoor environments, we utilize the Ultra-wideband (UWB) ranging system and fuse it with onboard IMU. Moreover, we propose our autonomous navigation architecture and demonstrate the versatility and impressive performance of our system for various autonomous missions.||URI:||http://scholarbank.nus.edu.sg/handle/10635/150349|
|Appears in Collections:||Ph.D Theses (Open)|
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