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About

Junqiao Zhao is a robotics and autonomous systems researcher whose work spans LiDAR-based localization, neural 3D mapping, multi-robot SLAM, and autonomous vehicle path planning. His most recognized contribution, LIO-Vehicle (2021, 22 citations), introduced a tightly-coupled vehicle dynamics extension of LiDAR inertial odometry, delivering highly accurate and real-time trajectory estimation tailored specifically for ground vehicles — addressing a notable gap in existing localization methods. Building on this foundation, Zhao has pioneered advances in implicit neural mapping, developing N³-Mapping (2024, 8 citations), which leverages normal-guided neural signed distance fields to achieve dense, accurate reconstruction in large-scale environments, and its successor UN3-Mapping (2025), which integrates uncertainty estimation for more reliable autonomous robot mapping. His work on multi-robot systems (2023, 7 citations) further demonstrates his breadth, proposing robust loop closure selection strategies that improve global map consistency across collaborative robot networks. Early contributions in autonomous path planning using cubic B-spline curves (2019) reflect his long-standing commitment to practical, deployable autonomy solutions. Collectively, Zhao's research addresses some of the most demanding challenges in autonomous navigation, establishing him as a meaningful contributor to the next generation of intelligent robotic systems.