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About

Mengchi Ai is a researcher specializing in localization, simultaneous localization and mapping (SLAM), and sensor fusion for autonomous systems operating in challenging, GNSS-denied environments. Her work addresses critical problems in robotics and autonomous vehicles, particularly in indoor and structured settings where traditional methods fail. She has made significant contributions by developing a LiDAR-inertial localization framework that leverages ground constraints for accurate real-time pose estimation within a pre-built point cloud map, a key enabler for smart city and AV applications. Ai also pioneered a direct sparse visual odometry approach that exploits structural regularities—such as lines and planes—to achieve robust performance in long corridor environments, overcoming the limitations of texture-based features. Her research extends to object-level SLAM, where she introduced a shaped-based, tightly coupled IMU/camera system that handles non-Gaussian error distributions for more reliable robot-environment interactions. Additionally, she has advanced indoor localization through a maximum likelihood particle filtering method that fuses direction-of-arrival beacons with IMU data. With over 15 citations across her most-cited works, Ai’s innovative algorithms are paving the way for more resilient and intelligent navigation systems in complex, real-world spaces.