Xinkai Kuang

Chinese Academy of Sciences, University of Science and Technology of China

Papers

Total Citations

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About

Xinkai Kuang is a rising researcher in robotics and autonomous systems, whose work focuses on advancing state estimation and perception for mobile robots. His primary research areas include simultaneous localization and mapping (SLAM), LiDAR-inertial odometry, and cooperative perception. Kuang’s most notable contribution is his paper "IGE-LIO: Intensity Gradient Enhanced Tightly Coupled LiDAR-Inertial Odometry" (2024), which has already garnered 21 citations—a strong indicator of its impact in the field. In this work, he addresses a critical limitation of traditional LiDAR SLAM methods, which rely solely on geometric features and often fail in degenerate environments. By integrating intensity gradient information, Kuang’s approach enhances localization accuracy and robustness, offering a significant improvement for mobile robot navigation in challenging scenarios. Additionally, his paper "Fast Clustering for Cooperative Perception Based on LiDAR Adaptive Dynamic Grid Encoding" (2023, 4 citations) explores efficient data processing for multi-robot systems. Kuang’s innovative solutions are paving the way for more reliable and resilient autonomous navigation, making him a promising voice in the next generation of SLAM researchers.