Xufang Ji

Ministry of Industry and Information Technology

Papers

Total Citations

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About

Xufang Ji is a researcher advancing the frontiers of autonomous navigation, with a primary focus on multi-sensor fusion, state estimation, and simultaneous localization and mapping (SLAM) for robots and unmanned aerial vehicles (UAVs). Ji’s most impactful work addresses the critical challenge of reliable localization in feature-poor indoor environments. Their highest-cited paper (25 citations, 2023) introduces an innovative 2D LiDAR SLAM method that leverages artificial landmarks to overcome the limitations of insufficient environmental features, a common bottleneck in indoor robotics. Building on this, Ji has developed sophisticated filtering techniques, including a variational Bayesian-based adaptive error-state Kalman filter (2024, 10 citations) that dynamically handles unknown and time-varying noise statistics—a significant improvement over conventional Kalman filters for real-world integrated navigation systems. For UAV applications, Ji proposed a fast and robust semidirect monocular visual-inertial odometry algorithm (2023, 9 citations) that achieves real-time pose estimation by balancing computational efficiency with tracking robustness. Collectively, Ji’s work demonstrates a clear trajectory toward practical, resilient navigation solutions that operate reliably under challenging, dynamic conditions, making substantial contributions to the fields of robotics and autonomous systems.