Alessandro Limongiello
Papers
2
Total Citations
15
H-Index
2
About
Alessandro Limongiello is a researcher whose work sits at the intersection of computer vision and autonomous mobile robotics, with a primary focus on real-time perception systems. His key contributions center on developing stereo-vision algorithms for moving object and obstacle detection (MOOD) in Autonomous Guided Vehicles (AGVs) and Autonomous Mobile Robots (AMRs). In his most cited work (2006, 8 citations), he presented a complete real-time system that calculates disparity maps to create 3D scene representations for obstacle recognition, strategically adapting and optimizing the best methods from the literature for practical deployment. His follow-up study (2007, 7 citations) introduced a novel approach to stereo matching specifically tailored for mobile robot applications, where he made the critical trade-off of sacrificing absolute accuracy for faster obstacle localization—a pragmatic insight essential for real-world navigation and collision avoidance. Though his citation counts are modest, Limongiello’s work is notable for its applied engineering focus, bridging the gap between theoretical computer vision and the hard real-time constraints of autonomous vehicle control. His research provides foundational building blocks for perception systems in industrial and service robotics.
Research Focus
Key Achievements
Top Papers
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- 2