Mingming Bai

Nanyang Technological University, Zhejiang University

Papers

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About

Mingming Bai is a rising researcher at the forefront of autonomous robotics, with key contributions in LiDAR odometry and multi-agent reinforcement learning (MARL). In their highly cited 2024 work, *I²EKF-LO: A Dual-Iteration Extended Kalman Filter Based LiDAR Odometry* (10 citations), Bai tackles a critical challenge in autonomous driving and mobile robotics: improving state estimation accuracy within the traditional Iterative Extended Kalman Filter (IEKF) framework. By introducing a dual-iteration strategy, Bai’s method offers a robust alternative to nonlinear optimization approaches, enhancing real-time performance and reliability. Complementing this, Bai’s *MAexp: A Generic Platform for RL-based Multi-Agent Exploration* (4 citations) addresses the persistent sim-to-real gap in multi-agent systems. This platform overcomes limitations in scene quantization and action discretization, providing a versatile, sampling-efficient environment for testing diverse MARL algorithms. Bai’s work bridges theoretical advances with practical deployment, demonstrating significant impact in both single-robot perception and collaborative exploration. As a young innovator, Bai is shaping the future of autonomous systems, with their research already cited by peers tackling real-world navigation and exploration challenges.