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As Austria's largest research and technology organisation for applied research, we are dedicated to make substantial contributions to solving the major challenges of our time, climate change and digitisation. To achieve our goals, we rely on our specific research, development and technology...
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firefighting semi-outdoor environments Solve challenges in feature searching and engineering for SLAM, providing robust perception results to enhance the SLAM framework for other researchers involved in
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. Compared to indoor environments, outdoor areas have uneven terrain and more unknown obstacles, which drastically reduces the performance of 2D LiDAR SLAM approaches. Instead, localization needs to capture
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Sciences (CNS) houses the King’s College (KCL) Neuroimaging Research Department and Major Research Facility (MRF) and the South London and Maudsley (SLAM) NHS Trust Neuroimaging Department. Patients and
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professional groups, through leadership and innovative teaching and research in the field of Sport Leadership and Management (SLAM). While we have a multi-disciplined team in SLAM, we currently have gaps in
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++; Knowledge of motor drivers and configuration; Experience with mechatronics design; Knowledge of cameras and LIDAR acquisition; Theoretical understanding of common algorithms used for navigation e.g. SLAM
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a project that involves simultaneous localization and mapping (SLAM) algorithms and deep learning object detection. The successful candidate will work with Prof. Danwei Wang and a group of Research
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. Prior experience in ROS/ROS2. Experience in Robotics algorithms such as Sensor Fusion, SLAM, Navigation, Modelling, Control, Manipulation. Excellent knowledge of the English language (written and verbal
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of data models such as logical / relational models, dimensional models and document models. Knowledge and experience with terrestrial and SLAM-based lidar data acquisition and data processing. Knowledge
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algorithms fusing multiple navigation and sensor sources with SLAM for handling of edge cases in vehicle navigation. Implement optimal path planning strategies to achieve efficient inspection coverage