Zhenbin Liu

China University of Mining and Technology

Papers

Total Citations

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About

Zhenbin Liu is a robotics researcher whose work focuses on advancing Simultaneous Localization and Mapping (SLAM) technology for autonomous navigation in complex environments. His primary research areas include multi-sensor fusion, visual-inertial navigation, and robust localization systems for mobile robots operating in both indoor and outdoor settings. Liu’s major contributions lie in developing SLAM frameworks that integrate complementary sensors—such as binocular vision, 2D lidar, and inertial measurement units (IMUs)—to overcome the limitations of individual sensors, particularly in challenging lighting conditions or texture-poor environments. His most cited paper, "Robust Visual-Inertial Navigation System for Low Precision Sensors under Indoor and Outdoor Environments" (2021, 26 citations), addresses the critical issue of scale ambiguity in monocular SLAM, while his subsequent work, "Fusion of binocular vision, 2D lidar and IMU for outdoor localization and indoor planar mapping" (2022, 22 citations), introduces the BVLI-SLAM scheme, demonstrating practical multi-modal fusion for seamless indoor-outdoor navigation. With a combined citation count of nearly 50 for his top papers, Liu’s research is gaining traction in the robotics community, offering scalable solutions for applications ranging from IoT to driverless vehicles. His work exemplifies the trend toward robust, low-cost sensor integration for real-world autonomous systems.