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About

Jingdi Cheng is a robotics researcher whose work focuses on advancing Simultaneous Localization and Mapping (SLAM) technologies for autonomous mobile robots. Their primary research areas include visual and laser SLAM, multi-robot collaborative mapping, and autonomous navigation systems. Cheng has made significant contributions to addressing fundamental challenges in SLAM, particularly in reducing cumulative drift through innovative back-end optimization algorithms that integrate vision with indoor positioning systems. Their work on multi-robot collaborative mapping, which incorporates integrated point-line features, represents an important advancement for large-scale environment mapping where single-robot systems fall short. With over 20 citations across their published works, Cheng's research on SLAM mapping and path planning simulation using the Robot Operating System (ROS) has provided practical frameworks for testing and validating autonomous navigation algorithms in real-world environments. Their investigations into motion distortion optimization for laser SLAM have helped improve the accuracy of lidar-based mapping systems. Cheng's comprehensive approach to SLAM challenges—spanning from simulation to optimization to multi-robot coordination—positions their work as valuable for researchers developing more robust and reliable autonomous navigation systems.