Xiaosheng Chen

Papers

Total Citations

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About

Xiaosheng Chen is a robotics researcher specializing in indoor simultaneous localization and mapping (SLAM), with a particular focus on improving the accuracy and efficiency of mobile robot navigation. His most cited work, "Indoor SLAM Algorithm Based on PL-ICP and Map Matching" (2021, 5 citations), addresses a fundamental challenge in robotics: how to enable robots to build consistent maps of unknown indoor environments while tracking their own position. Chen’s key contribution lies in advancing the Point-to-Line Iterative Closest Point (PL-ICP) algorithm, an optimized variant of the standard ICP method. While traditional ICP aligns point clouds by minimizing point-to-point distances, PL-ICP achieves faster convergence and higher precision by minimizing point-to-line distances, making it particularly effective for structured indoor spaces. Chen’s work demonstrates how this enhanced algorithm, combined with robust map matching techniques, can significantly improve the reliability of real-time SLAM systems. Though his citation count is modest, his research addresses a critical bottleneck in autonomous robotics—balancing computational efficiency with mapping accuracy—and contributes to the broader goal of enabling robots to operate safely and autonomously in human-centric environments.