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About

Chris Xiaoxuan Lu is a prominent researcher specializing in mobile sensing, robot perception, and deep learning-based localization and mapping. His work sits at the intersection of inertial navigation, simultaneous localization and mapping (SLAM), and multimodal sensor fusion, with a particular focus on enabling robust spatial intelligence for mobile robots, autonomous vehicles, and IoT devices. Lu's most influential contributions include pioneering deep learning approaches to pedestrian inertial navigation (154 citations) and the development of milliEgo (130 citations), which leverages millimeter-wave radar for robust trajectory estimation — pushing beyond the limitations of conventional optical methods. His widely read surveys on deep learning for localization and mapping (106 and 69 citations) have become essential references for researchers entering this rapidly evolving field. He has also advanced perception in visually-degraded environments through thermal-inertial odometry systems (82 citations) and probabilistic SLAM frameworks (46 citations), addressing real-world challenges such as smoke, darkness, and low visibility. Beyond navigation, Lu has explored mmWave radar for contactless heart rate monitoring (70 citations) and adaptive sensor fusion strategies for autonomous systems (36 citations). The release of the OxIOD dataset (60 citations) further demonstrates his commitment to enabling reproducible, community-driven research. Collectively, his work has garnered over 800 citations, establishing him as a leading voice in spatial machine intelligence.