Papers
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Total Citations
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About
Peimin Liu is a researcher at the forefront of robotics and autonomous systems, with a primary focus on simultaneous localization and mapping (SLAM) and 3D scene understanding. Her major contribution lies in bridging the gap between traditional geometric SLAM and modern semantic perception. In her most cited work, she introduced a novel framework that fuses 3D point cloud localization with deep learning-based semantic segmentation, enabling robots to build high-fidelity, semantically rich maps of unknown environments. Specifically, she developed HieSemNet, a hierarchical deep network that integrates multi-scale sparse and dense features to extract real-time semantic information from RGB-D data. This approach significantly enhances a robot’s ability to interpret complex scenes and reconstruct detailed 3D maps, addressing critical limitations in existing SLAM algorithms regarding scene detail and map completeness. While her citation count is currently modest, her work represents a meaningful step toward more intelligent and context-aware robotic navigation. Her research is particularly relevant for applications in autonomous exploration, service robotics, and environmental monitoring, where understanding both geometry and semantics is essential for robust performance.
Research Focus
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Top Papers
- 1结合语义信息与3D点云技术的未知环境地图构建方法1 citations · 2023