Meixuan Ren

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About

Meixuan Ren is a robotics researcher specializing in multi-sensor fusion for autonomous navigation, with a focus on LiDAR localization, visual-inertial odometry, and real-time 3D reconstruction. Their work addresses critical challenges in mobile robot localization in GNSS-denied environments, particularly for indoor and dense construction scenarios. Ren’s most cited paper (51 citations) introduces an improved LiDAR localization method that integrates 3D LiDAR, IMU, and odometer data using enhanced Adaptive Monte Carlo Localization (AMCL), enabling robust positioning without satellite signals. They further advanced visual-inertial systems with an enhanced hybrid odometry framework (18 citations) that fuses camera and IMU data for superior accuracy and resilience. Their notable 2023 work on real-time dense 3D reconstruction (17 citations) combines direct visual-inertial odometry with deep multiview stereo networks, achieving metric-scale reconstruction—a significant step for autonomous construction and inspection tasks. Ren’s contributions bridge classical estimation algorithms with modern deep learning, offering practical solutions for real-world robotics deployment.