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About

Tianrui Guan is a robotics and computer vision researcher whose work sits at the intersection of autonomous navigation, terrain perception, and aerial video understanding. His research primarily focuses on enabling robots to navigate safely and efficiently through complex, unstructured outdoor environments — a challenge demanding robust perception across diverse and unpredictable terrains. Guan's most influential contribution, GA-Nav (2022), introduced a group-wise attention mechanism for terrain segmentation that allows robots to classify navigable regions from RGB images, earning 145 citations and establishing him as a key voice in outdoor robot perception. Building on this foundation, he has developed a series of complementary systems: GrASPE fuses multimodal sensory inputs including cameras, LiDAR, and odometry for trajectory traversability estimation, while VERN tackles dense vegetation environments using few-shot learning. His VINet framework further advances terrain classification by coupling visual and inertial signals for generalization across unknown surfaces. Beyond ground navigation, Guan has made notable contributions to aerial video action recognition through AZTR and SCP, demonstrating impressive versatility. His work on crowd navigation — including DenseCAvoid and OF-VO — reflects a consistent commitment to real-world robot deployment with commodity sensors. Collectively, his publications have accumulated over 260 citations, marking Guan as an emerging and productive force in field robotics research.