Lianjie Sui
Papers
2
Total Citations
6
H-Index
2
About
Lianjie Sui is a researcher focused on advancing autonomous navigation for mobile robots, with a particular emphasis on LiDAR-based SLAM (Simultaneous Localization and Mapping) and environmental perception. His work centers on developing robust algorithms for extracting and fitting linear features from laser radar scan data—a critical component for real-time localization and map-matching in unknown environments. In his 2022 paper, "An Adaptive Threshold Line Segment Feature Extraction Algorithm for Laser Radar Scanning Environments," Sui addressed the challenge of noise and sparse data in LiDAR scans, proposing an adaptive method to improve map accuracy for autonomous navigation. Building on this, his 2023 work, "A Radar Linear Feature Fitting Algorithm Combining Adaptive Clustering and Corner Detection Operator," introduced a novel integration of clustering and corner detection to enhance feature extraction efficiency. While his citation counts are currently modest (3 citations each), these contributions are foundational for researchers tackling the practical hurdles of LiDAR-based navigation, such as sensor noise and data sparsity. Sui’s work is particularly notable for its focus on adaptive thresholding and clustering techniques, offering scalable solutions for real-time robotic applications. His research is a valuable resource for students and engineers working on autonomous systems, providing practical algorithms that bridge the gap between raw sensor data and reliable navigation.
Research Focus
Key Achievements
Top Papers
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- 2