Beiya Yang

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About

Beiya Yang has made significant contributions to the field of visual simultaneous localisation and mapping (VSLAM), with a particular focus on enhancing the robustness of monocular systems in challenging real-world conditions. Yang’s key research areas include adaptive feature extraction, image enhancement, and the development of resilient SLAM algorithms for intelligent mobile robots operating in complex lighting environments. Their most notable work, "AFE-ORB-SLAM: Robust Monocular VSLAM Based on Adaptive FAST Threshold and Image Enhancement for Complex Lighting Environments" (2022), has garnered 38 citations, demonstrating its impact on advancing SLAM technology. This paper introduces an innovative approach that dynamically adjusts the FAST corner detection threshold and applies image enhancement techniques to maintain accuracy and stability even under severe illumination variations—a critical limitation of traditional feature-based SLAM systems. By addressing the fragility of visual odometry in low-light or high-contrast scenes, Yang’s research directly supports the deployment of autonomous robots in indoor, outdoor, and industrial environments where lighting is unpredictable. Their work stands as a valuable resource for researchers and engineers seeking to build more reliable perception systems for mobile robotics.