Lei Rao

Papers

Total Citations

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About

Lei Rao is a robotics researcher specializing in visual simultaneous localization and mapping (VSLAM) for mobile robots, with a particular focus on legged platforms. Their work addresses critical challenges in autonomous navigation, including robust localization in complex environments and sensor fusion for difficult materials. Rao’s most cited paper, "Sampling visual SLAM with a wide‐angle camera for legged mobile robots" (2022, 6 citations), introduces a novel approach using wide-angle cameras to enhance field of view and feature richness, improving localization accuracy for legged robots. Building on this, their 2023 paper "An End‐to‐End Robotic Visual Localization Algorithm Based on Deep Learning" (4 citations) overcomes limitations of traditional point feature matching, offering a more robust solution for dynamic or texture-poor environments. Another notable contribution, "A Glass Detection Method Based on Multi-sensor Data Fusion in Simultaneous Localization and Mapping" (2023, 2 citations), tackles the challenging problem of detecting transparent surfaces—a common failure point in SLAM systems—by integrating multiple sensor inputs. Together, these works demonstrate Rao’s impact in advancing practical, real-world SLAM systems, pushing toward more reliable autonomous navigation in unstructured settings.