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localization and mapping (SLAM) algorithm development & implementation for the firefighting scenarios where the noises caused by fire and water spray can degrade the mainstreamed SLAM algorithms severely
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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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Machine Learning (ML) algorithm and 3D SLAM for automotive robot navigation etc Job Requirements: A Ph.D degree in Electrical & Electronics Engineering or Computer Engineering Relevant experience and
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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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robotic manipulator and an omnidirectional autonomous Automated Guided Vehicle Work involves SLAM and perception algorithm development, sensor fusion, test rig set-up, and experimental validation Job
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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
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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
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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
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(NUS). The Research Engineer will work closely with the Principal Investigator (PI) on quadrotor path planning, control, SLAM, swarm path planning, and cooperative transport. The approach will leverage
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fast prototyping. • Has experience in robotics frameworks, especially ROS. • Demonstrated experience in visual, LiDAR, and multi-sensor fusion SLAM algorithms. • Experienced in 3D Reconstruction