Haoxuan Jiang

Hong Kong University of Science and Technology

Papers

Total Citations

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About

Haoxuan Jiang is a researcher advancing the field of robotics and automation through innovative work in visual localization and semantic perception. His primary research areas include marker-based localization, scene understanding, and robust data association for autonomous systems operating in dynamic environments. Jiang’s major contribution lies in developing visual-marker-based localization methods tailored for flat-variation scenes—environments where traditional approaches falter due to appearance changes, such as shifting lighting or worn road markings. By integrating semantic cues, his work filters out unreliable data from moving vehicles and degraded surfaces, significantly improving localization accuracy and reliability. His most-cited paper, "Visual-Marker-Based Localization for Flat-Variation Scene" (2024, 8 citations), exemplifies this approach, offering a practical solution to a persistent challenge in real-world deployment. Though early in his career, Jiang’s focus on semantic filtering marks a notable step toward more resilient autonomous navigation. His research holds promise for applications in logistics, urban mapping, and field robotics, where environmental variability is the norm.