Zhiteng Li

Papers

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About

Zhiteng Li is a robotics researcher advancing autonomous aerial systems through real-time environmental perception. His work centers on semantic dense mapping for unmanned aerial vehicles (UAVs), enabling drones to understand not just their location but the meaning of their surroundings—a critical capability for autonomous tasks in rescue, inspection, and agriculture. His most cited paper, "RTSDM: A Real-Time Semantic Dense Mapping System for UAVs" (2022), has garnered 14 citations, demonstrating its emerging impact in the field. This contribution addresses the fundamental challenge of equipping flying robots with the ability to simultaneously localize themselves and interpret scene semantics in real time, bridging the gap between raw sensor data and actionable understanding. Li's research sits at the intersection of computer vision, robotics, and embedded systems, pushing toward practical deployment of intelligent drones that can navigate complex environments without human intervention. His work is particularly relevant for students and researchers interested in autonomous navigation, SLAM, and the integration of semantic reasoning into resource-constrained robotic platforms.