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About

Riccardo Monica is a leading researcher in autonomous robotics, with a primary focus on 3D perception, next-best view (NBV) planning, and intelligent manipulation for industrial automation. His most influential work, "Contour-based next-best view planning from point cloud segmentation of unknown objects" (53 citations), introduced a novel method for efficiently reconstructing unknown 3D objects by selecting optimal sensor viewpoints based on contour analysis. This contribution significantly advanced the field of automated 3D reconstruction, addressing the computationally expensive view simulation step in NBV planning. Monica further refined these techniques with his "Surfel-Based Next Best View Planning" (32 citations) and a deep learning approach in "A Probabilistic Next Best View Planner for Depth Cameras Based on Deep Learning" (16 citations). Beyond perception, he has made substantial contributions to industrial robotics, particularly in autonomous depalletizing. His work on "Integration of a Multi-Camera Vision System and Admittance Control for Robotic Industrial Depalletizing" (29 citations) and "Toward Future Automatic Warehouses" (28 citations) demonstrates a complete pipeline from 3D vision to manipulation, enabling mobile manipulators to autonomously unload pallets. With over 200 total citations, Monica’s research bridges the gap between theoretical planning algorithms and practical, deployable robotic systems for logistics and manufacturing.