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About

Chi Guo is a robotics researcher whose work centers on state estimation, simultaneous localization and mapping (SLAM), and sensor fusion for autonomous systems. His most impactful contribution is a 2D LiDAR-SLAM algorithm that integrates deep visual loop closure detection, enabling robust navigation in indoor environments where geometric features alone are insufficient—a paper that has garnered 14 citations since 2023. Guo has also made foundational contributions to the mathematical underpinnings of robotics, providing a detailed derivation of the geometry and kinematics of the SE_K(3) matrix Lie group, which is essential for uncertainty representation in state estimation problems (9 citations). His work extends to learning-based visual inertial odometry, where he developed a right-invariant SE₂(3)-EKF for relative navigation that improves robustness without requiring sensor calibration. Most recently, his 2025 paper on SDS-SLAM fuses static and dynamic semantic information for driving scenarios, addressing the critical challenge of dynamic object perception in autonomous driving. Through these contributions, Guo bridges theoretical Lie group mathematics with practical SLAM systems, advancing both the foundations and applications of robot perception.