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About

Han-Pang Chiu is a robotics and computer vision researcher whose work spans autonomous navigation, multi-sensor fusion, and semantic scene understanding. His research addresses some of the most challenging problems in mobile robotics, particularly enabling reliable robot navigation in GPS-denied and visually-degraded environments where conventional methods falter. Chiu's most influential contribution, "Constrained Optimal Selection for Multi-Sensor Robot Navigation Using Plug-and-Play Factor Graphs" (2014, 62 citations), introduced a flexible real-time framework allowing robots to dynamically integrate diverse sensor modalities while satisfying performance constraints — a significant advance in robust autonomous navigation. Building on this foundation, his subsequent work on multi-sensor fusion and UWB-aided navigation extended these capabilities to low-cost sensor platforms for infrastructure inspection and rescue applications. More recently, Chiu has embraced deep learning and semantic reasoning, developing systems like MaAST — leveraging transformer architectures for efficient visual navigation — and SIGNAV, which brings semantic SLAM capabilities to degraded visual conditions. His latest work, Graph2Nav, pushes further toward rich 3D scene understanding through object-relation graphs. Collectively, his research bridges classical robotics principles with modern machine learning, making meaningful contributions to the autonomous systems community across more than a decade of sustained scholarship.