Yongping Zhai

National University of Defense Technology

Papers

Total Citations

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About

Yongping Zhai’s research focuses on advancing robotic perception and autonomous navigation, with key contributions in simultaneous localization and mapping (SLAM) and real-time object detection. His most-cited work, “RGB-D SLAM Using Point–Plane Constraints for Indoor Environments” (2019, 19 citations), introduced a novel method that leverages both point and plane features from RGB-D cameras to simultaneously estimate robot poses and reconstruct environmental maps. This approach significantly improves pose estimation accuracy and map robustness in indoor settings, addressing fundamental challenges for robotic autonomous behavior. More recently, Zhai has extended his expertise to underwater robotics, as demonstrated in “Real-time underwater target detection based on improved YOLOv7” (2025, 10 citations), where he enhanced a state-of-the-art deep learning framework to achieve rapid and reliable detection in challenging aquatic environments. His work bridges geometric SLAM techniques with modern deep learning, offering practical solutions for real-world robotic systems. With a growing citation record and applications spanning indoor navigation to marine exploration, Zhai is establishing himself as a versatile researcher in computer vision and robotics, contributing to the next generation of autonomous systems.