Paolo Pugliese
Papers
15
Total Citations
165
H-Index
5
About
Paolo Pugliese is an Italian researcher whose work sits at the intersection of mobile robotics, state estimation, and autonomous navigation. His research has focused primarily on solving two of the most fundamental challenges in robotics: localization—determining where a robot is in space—and Simultaneous Localization and Mapping (SLAM), which enables robots to build maps of unknown environments while tracking their own position within them. Pugliese's most significant contributions center on the application and comparative analysis of advanced filtering techniques, particularly the Extended Kalman Filter (EKF) and Unscented Kalman Filter (UKF). His 2015 paper comparing these two approaches using real experimental data has accumulated 74 citations, establishing it as a key reference in the field. His earlier SLAM work explored innovative environment representations, including segment-based and polynomial-based mapping algorithms, offering flexible solutions that require no prior assumptions about the robot's workspace. Beyond filtering theory, Pugliese has contributed to practical sensor fusion strategies, incorporating ultrasonic sensors, wireless distance measurements, and intelligent sensor-switching policies to improve localization robustness under real-world conditions. His body of work, spanning nearly two decades from control systems to modern robotics, reflects a consistent commitment to bridging rigorous mathematical estimation theory with hands-on experimental validation—making his research particularly valuable for engineers and students working on autonomous mobile systems.
Research Focus
Key Achievements
Top Papers
- 1Mobile robot localization via EKF and UKF: A comparison based on real data74 citations · 2015
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