Luigi Chisci

About

Luigi Chisci is a leading researcher in autonomous systems and sensor fusion, with a focus on Bayesian filtering for robotics and multi-agent navigation. His work centers on developing advanced Kalman filtering techniques—particularly the unscented Kalman filter (UKF)—to solve critical challenges in autonomous underwater vehicles (AUVs), multi-vehicle simultaneous localization and mapping (SLAM), and swarm localization. His most cited paper, "An unscented Kalman filter based navigation algorithm for autonomous underwater vehicles" (2016, 96 citations), provides a robust navigation strategy for AUVs operating in GPS-denied underwater environments, addressing a key bottleneck in marine robotics. He also pioneered a random set approach to distributed multivehicle SLAM (2017, 23 citations), enabling teams of robots to cooperatively build environmental maps without centralized control. Additionally, his work on tactile object recognition (2016) introduces novel Bayesian filtering to enhance robotic perception beyond vision, while his research on swarm localization (2009) applies UKF to wireless sensor networks for urban scenarios. With a consistent record of high-impact contributions, Chisci's innovations in estimation theory and autonomous navigation continue to influence both academic research and practical robotics applications.

Research Focus

Key Achievements

Top Papers

1. 1
   An unscented Kalman filter based navigation algorithm for autonomous underwater vehicles

   96 citations · 2016
2. 2
   Random Set Approach to Distributed Multivehicle SLAM

   23 citations · 2017
3. 3
   A novel Bayesian filtering approach to tactile object recognition

   7 citations · 2016
4. 4
   Localization of a Swarm of Mobile Agents via Unscented Kalman Filtering

   6 citations · 2009
5. 5
   An Innovative Navigation Strategy for Autonomous Underwater Vehicles: An Unscented Kalman Filter Based Approach

   4 citations · 2015

Key Collaborators

Contact & Links