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About

Xixiang Liu is a leading researcher in mobile robotics, specializing in autonomous navigation, path planning, and visual SLAM (Simultaneous Localization and Mapping). Their work addresses critical challenges in robot perception and motion in complex, dynamic, and low-illumination environments. Liu’s major contributions include the development of the FPS algorithm, a fast path planner that leverages sparse visibility graphs and bidirectional breadth-first search to dramatically reduce search overhead compared to traditional occupancy grid maps. They also pioneered ULG-SLAM, an unsupervised learning framework that fuses geometric features with deep learning to enhance robot localizability estimation, achieving over 14 citations since 2024. For dynamic scenes, Liu introduced a CO-HDC instance segmentation network to eliminate dynamic feature points, improving SLAM accuracy. Their work on efficient path planning using laser SLAM and optimized visibility graphs has garnered 11 citations, while their fusion of point and line features in visual inertial odometry advances navigation in dim lighting. With over 70 total citations across seven key publications, Liu’s innovations in sampling-based motion planning (e.g., Bi-HS-RRT$^\text{X}$) and high-performance torque control for legged robots underscore their impact on both theoretical and applied robotics.