Longjie Chen

Guilin University of Electronic Technology

Papers

Total Citations

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About

Longjie Chen is a leading researcher in acoustic sensing and robotic perception, with a focus on simultaneous localization and mapping (SLAM) in challenging, vision-denied environments. His most cited work introduces a graph optimization-based acoustic SLAM edge computing system that achieves centimeter-level mapping accuracy by leveraging echo signals and reflector recognition. This innovation addresses a critical gap: enabling robots to navigate and map unknown spaces when cameras are unavailable, such as in smoke-filled or dark areas. By refining time-of-arrival (TOA) estimation from room impulse responses (RIRs), Chen’s system enhances the association of acoustic reflections with physical surfaces, pushing the boundaries of autonomous navigation. His contributions have garnered attention in the robotics and edge computing communities, with his flagship paper accumulating citations that underscore its practical impact. Chen’s work not only advances the theoretical foundations of acoustic SLAM but also offers deployable solutions for real-world applications, including search-and-rescue and industrial inspection. As a researcher, he continues to bridge the gap between acoustic signal processing and robotic autonomy, making him a notable figure in the field.