Papers
1
Total Citations
40
H-Index
1
About
Huabo Shen is a prominent researcher in the fields of computer vision, robotics, and autonomous navigation, with a particular focus on simultaneous localization and mapping (SLAM) in dynamic environments. His most notable contribution is the development of a dense point cloud SLAM method that integrates an improved YOLOV8 object detection framework with the ORB-SLAM3 system, enabling robust performance in complex, changing surroundings. This work, published in 2024 and already garnering 40 citations, addresses a critical challenge in real-world robotics and augmented reality applications—handling dynamic objects that degrade traditional SLAM accuracy. By fusing deep learning-based detection with geometric mapping, Shen has advanced the reliability of dense 3D reconstruction in cluttered spaces. His research bridges the gap between efficient visual odometry and semantic understanding, offering practical solutions for autonomous vehicles, drones, and mobile robots. With a growing citation count reflecting the timeliness and utility of his work, Shen is establishing himself as an innovator at the intersection of deep learning and spatial intelligence, making his contributions essential reading for students and researchers tackling real-world SLAM problems.
Research Focus
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Top Papers
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