Fengkui Cao
Papers
5
Total Citations
33
H-Index
3
About
Fengkui Cao is a robotics researcher whose work centers on autonomous navigation, LiDAR perception, and simultaneous localization and mapping (SLAM) for mobile robots operating in complex, dynamic environments. His major contributions lie in developing real-time, efficient solutions for small object detection and robust SLAM under challenging conditions. His most cited work, "ScorePillar" (2024, 14 citations), introduces a pillar-scoring method for LiDAR that enables real-time detection of sparse, vulnerable objects like pedestrians—a critical capability for safe robot navigation. Cao has also advanced SLAM technology with "TRLO" (2025, 9 citations), which integrates 3D dynamic object tracking and removal to overcome the limitations of static-environment assumptions in urban settings, and "BEV-LSLAM" (2025, 6 citations), a compact bird’s-eye-view LiDAR SLAM system designed for outdoor autonomy. Further notable achievements include a robust ground-constrained SLAM method for sparse-channel LiDAR and an adaptive path-planning algorithm for human-robot collaboration in intelligent wheelchair systems. With a growing citation record and a focus on practical, deployable robotics, Cao is making impactful strides toward safer, more reliable autonomous systems in real-world environments.
Research Focus
Key Achievements
Top Papers
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- 3BEV-LSLAM: A Novel and Compact BEV LiDAR SLAM for Outdoor Environment6 citations · 2025
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