Jicheng Dai
Papers
2
Total Citations
33
H-Index
2
About
Jicheng Dai is a researcher advancing the frontiers of 3D perception and autonomous navigation, with key contributions in point cloud registration, LiDAR-inertial odometry, and dynamic scene understanding. His work addresses critical challenges in robotics and geospatial mapping, particularly the robust handling of moving objects and complex environments. Dai’s most cited paper, "A hierarchical multiview registration framework of TLS point clouds based on loop constraint" (2022, 27 citations), introduces a novel method for aligning terrestrial laser scanning data using loop constraints, significantly improving accuracy and scalability in large-scale 3D reconstruction. In his second notable work, "A Novel Lidar Inertial Odometry with Moving Object Detection for Dynamic Scenes" (2022, 6 citations), Dai tackles a fundamental limitation of simultaneous localization and mapping (SLAM) systems—their vulnerability to dynamic objects. By integrating moving object detection into LiDAR-inertial odometry, his approach enhances robustness in highly dynamic environments, enabling more reliable motion planning and trajectory prediction for robots. These contributions demonstrate Dai’s impact on both theoretical frameworks and practical applications, offering solutions that push the boundaries of autonomous systems in real-world, unpredictable settings.
Research Focus
Key Achievements
Top Papers
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