Xiaomei Xiao
Papers
2
Total Citations
10
H-Index
1
About
Xiaomei Xiao is a rising researcher in the field of robotic perception, with a focused expertise in visual simultaneous localization and mapping (SLAM) for dynamic environments. Her work directly addresses a critical limitation of traditional SLAM systems, which often fail in real-world settings populated by moving objects. Xiao’s major contributions lie in developing novel frameworks that enhance SLAM robustness by integrating deep learning with geometric constraints. Her 2024 paper, "OMS-SLAM," introduces a method that combines object detection with multiple geometric feature constraints and statistical threshold segmentation to effectively filter out dynamic objects, achieving 9 citations and establishing a new standard for dynamic scene handling. Building on this, her 2025 work, "IBR-SLAM," further refines the approach by employing an improved BiSeNet segmentation network with RGB-D sensors to mitigate feature point mapping errors. Through these innovations, Xiao is pioneering more reliable navigation systems for mobile robots operating in complex, unpredictable environments, marking her as an emerging authority in advancing SLAM technology beyond static laboratory conditions.
Research Focus
Key Achievements
Top Papers
- 1
- 2IBR-SLAM: visual SLAM based on improved BiSeNet with RGB-D sensor1 citations · 2025