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develop Localization and Perception System based on Multi-sensor Fusion Job Description: Responsible for development of robust and reliable sensor fusion algorithms for localization and navigation
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regardless of viewpoint (calibration and synchronization). Perform LiDAR point cloud fusion, temporal fusion for SLAM. Perform the processing of the fused data (sensor fusion and feature fusion). Analyze
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Multi-Sensor Data Fusion and Smart Decision Making School of Electrical and Electronic Engineering PhD Research Project Self Funded Prof Lyudmila Mihaylova Application Deadline: Applications
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between denoising, alignment, and fusion can also be considered, rather than treating each module independently. The goal of the thesis is to develop multi-sensor image fusion tools that address all these
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requirements of the developed system under realistic outdoor conditions; Investigating the use of multiple odometry sources and multimodal sensor fusion techniques, including, but not limited to
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; • Knowledge of navigation, localization, state estimation, GNSS, IMU, Visual-Inertial Odometry, LiDAR, and sensor fusion techniques; • Knowledge of detection, classification, tracking, and validation of
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, including operational modes, robust state estimation, trajectory planning, sensor integration, data acquisition and fusion, and criteria for assessing the structural condition of the wind turbine
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-denied and electronically contested environments, leveraging Vision-Language-Action (VLA) models, multi-sensor fusion (LiDAR, radar, cameras, IMU), semantic SLAM, and EW-resilient mission planning. The
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system identification through lab-scale and field experiments. Key Responsibilities: Develop algorithms for guided-wave analysis, response analysis, sensor fusion, and system identification using
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of state estimation, sensor fusion or robot perception, and documented experience of experimental work with drones. Previous work with experimentally verified results in at least one of the research