Tiziano Guadagnino
University of Bonn, Sapienza University of Rome, Robotics Research (United States)
Papers
27
Total Citations
941
H-Index
13
About
Tiziano Guadagnino is a robotics researcher whose work centers on mobile robot navigation, 3D perception, and simultaneous localization and mapping (SLAM). He is perhaps best known for KISS-ICP, a deceptively simple yet highly effective point cloud registration framework that demonstrated that stripping away complexity — rather than adding it — can yield superior odometry performance. Published in 2023, the work has already garnered over 430 citations, establishing it as a landmark contribution to the LiDAR odometry community. Guadagnino's broader research portfolio spans volumetric mapping with tools like VDBFusion, neural distance field representations for localization, LiDAR-inertial odometry, and long-term localization in dynamic or changing environments. He has also ventured into agricultural robotics, contributing a hierarchical segmentation framework for plant phenotyping. Across his publications, a consistent theme emerges: making robust, practical systems that work reliably in real-world conditions rather than controlled benchmarks. With nearly 800 citations accumulated across a focused body of work, Guadagnino has quickly positioned himself as an influential voice in mobile robotics, particularly for researchers tackling the enduring challenges of reliable autonomous navigation.
Research Focus
Key Achievements
Top Papers
- 1
- 2VDBFusion: Flexible and Efficient TSDF Integration of Range Sensor Data84 citations · 2022
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- 5Long-Term Localization Using Semantic Cues in Floor Plan Maps39 citations · 2022
- 6IR-MCL: Implicit Representation-Based Online Global Localization32 citations · 2023
- 7LocNDF: Neural Distance Field Mapping for Robot Localization31 citations · 2023
- 8Effectively Detecting Loop Closures using Point Cloud Density Maps27 citations · 2024
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