Xiangli Yang

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About

Xiangli Yang is a researcher advancing the frontiers of cross-modal perception for autonomous systems. Her primary research areas include visual localization, multimodal learning, and robotic navigation. She made a significant contribution with her 2023 paper "Cross-Modal 2D-3D Localization with Single-Modal Query," which tackles a critical bottleneck in real-world robotics: the modality mismatch between query and database data. By enabling place recognition across different data types (e.g., 2D images to 3D point clouds), her work directly supports applications in SLAM and autonomous navigation, allowing robots to operate more flexibly in diverse environments. Though early in her career, this work has already garnered 9 citations, signaling its relevance to the geoscience and robotics communities. Yang’s research addresses a practical challenge that could make autonomous systems more robust and adaptable, bridging the gap between theoretical computer vision and real-world deployment. Her focus on cross-modal localization positions her as an emerging voice in making robots smarter and more versatile in unstructured settings.