Hailan Kuang

Papers

Total Citations

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About

Hailan Kuang is a researcher whose work focuses on advancing visual simultaneous localization and mapping (SLAM) for mobile robots, with particular emphasis on monocular camera systems. Her major contributions center on improving the accuracy and efficiency of SLAM algorithms, addressing fundamental challenges such as depth estimation, keyframe selection, and matching errors in ORB-SLAM systems. Kuang’s research demonstrates a practical approach to enhancing robot perception by integrating depth information through saliency detection and scene preprocessing, which helps overcome the inherent limitations of monocular cameras that cannot directly capture depth data. Her most cited work, “Monocular SLAM Algorithm Based on Improved Depth Map Estimation and Keyframe Selection” (2018), has garnered 4 citations and addresses a critical bottleneck in modern robotics—the need for simple, cost-effective navigation systems. Her subsequent paper on improved ORB-SLAM for mobile robots (2019) further refines these techniques, targeting real-world problems like slow operation speed and limited map applicability. While her citation counts are modest, Kuang’s contributions are technically significant for researchers working on low-cost robotic perception systems, particularly in environments where stereo or RGB-D cameras are impractical.