Kehua Zhang

Papers

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About

Kehua Zhang is a leading researcher in visual simultaneous localization and mapping (SLAM), with a focused expertise on enhancing robotic perception in dynamic and unstructured environments. Their major contributions center on developing robust SLAM systems that maintain high localization accuracy despite the presence of moving objects—a critical challenge for real-world robotics. Zhang’s most cited work, “DN-SLAM: A Visual SLAM With ORB Features and NeRF Mapping in Dynamic Environments” (2023, 67 citations), pioneers the integration of Neural Radiance Fields (NeRF) for dense mapping within a dynamic SLAM framework, addressing both accurate pose estimation and map consistency. Complementing this, their paper “An Adaptive ORB-SLAM3 System for Outdoor Dynamic Environments” (2023, 23 citations) introduces novel methods to correct epipolar geometry errors caused by dynamic objects, significantly boosting localization reliability in outdoor settings. With a growing citation impact, Zhang’s work is instrumental for autonomous navigation in robotics, offering practical solutions for vehicles and drones operating in unpredictable surroundings. Their research stands out for bridging advanced neural rendering with classical SLAM, marking a notable achievement in the field.