Po‐Ting Chen

Papers

Total Citations

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About

Po-Ting Chen’s research lies at the intersection of autonomous navigation, spacecraft landing systems, and robotic locomotion. His most impactful work, “Feature-Based Scanning LiDAR-Inertial Odometry Using Factor Graph Optimization” (2023, 18 citations), addresses a critical challenge in mobile robotics: correcting motion-distorted scans from scanning LiDAR sensors. By integrating inertial data through factor graph optimization, Chen’s approach enables robust, drift-free localization for robots operating without absolute position sensors—a key enabler for autonomous exploration in GPS-denied environments. Chen also contributed to NASA’s SPLICE project, developing simulation frameworks for precision lunar landing and hazard avoidance (2020, 7 citations). This work supports NASA’s Artemis program by advancing autonomous spacecraft navigation technologies. Earlier in his career, he explored creative robotic design with a two-wheeled, ball-flinging robot (2010, 2 citations), optimizing its throwing distance through simulation and control. With a total of 27 citations across his most-cited works, Chen’s contributions demonstrate a rare breadth—from foundational odometry algorithms to applied space systems and playful robotics. His research continues to shape how robots perceive and navigate challenging environments, whether on Earth or the Moon.