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About

Sam Zeng is a leading researcher in robotics and autonomous systems, with a primary focus on robust localization, sensor fusion, and vision-based navigation for unmanned aerial vehicles (UAVs). His most influential work, "Robust localization and localizability estimation with a rotating laser scanner" (2017, 106 citations), introduces a novel approach that fuses inertial measurement unit (IMU) data with a rotating laser scanner using an Error State Kalman Filter (ESKF) combined with a Gaussian Particle Filter (GPF). This work significantly advances the reliability of state estimation in challenging environments. Zeng is also a pioneer in vision-based autonomous flight, notably with his 2016 paper "Vision and Learning for Deliberative Monocular Cluttered Flight" (46 citations), which presents the first implementation of receding horizon control using monocular vision as the sole sensor for UAVs navigating cluttered spaces. His research on introspective perception (2016, 8 citations) further demonstrates his commitment to building resilient autonomous systems capable of self-assessing their own performance. With over 169 total citations, Zeng’s contributions are shaping the future of robust, perception-driven robotics.