Papers
9
Total Citations
128
H-Index
6
About
Simone Gasparini is a leading researcher in mobile robotics and computer vision, with a focus on autonomous mapping and 3D scene reconstruction. His most influential work addresses the fundamental challenge of building geometric maps without relying on odometry or pose estimates—a critical capability for robots operating in unknown or GPS-denied environments. His highly cited 2006 paper (44 citations) introduced novel methods for integrating laser scans into segment-based maps without pose information, while his 2005 work on localizing straight lines from single 2D images (22 citations) advanced single-view 3D reconstruction, offering alternatives to stereo-vision approaches. Gasparini also made significant contributions to experimental methodology in robotics, proposing replicable standards for mapping research (21 citations). His work on reducing line segments in maps and merging partial maps without odometry further solidified his reputation in efficient environmental representation. Notably, he explored catadioptric camera systems for line localization and uncalibrated visual odometry, demonstrating versatility across sensing modalities. With over 128 citations across his top papers, Gasparini’s research has provided foundational techniques for autonomous navigation, particularly in scenarios where traditional localization methods fail, making his work essential reading for students and researchers in robotic perception and mapping.
Research Focus
Key Achievements
Top Papers
- 1Building Segment-Based Maps Without Pose Information44 citations · 2006
- 2On the Localization of Straight Lines in 3D Space from Single 2D Images22 citations · 2005
- 3Good Experimental Methodologies for Robotic Mapping: A Proposal21 citations · 2007
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- 5Merging Partial Maps Without Using Odometry9 citations · 2005
- 6Map building without odometry information9 citations · 2004
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