Diego Viejo
Papers
9
Total Citations
118
H-Index
6
About
Diego Viejo is a robotics researcher whose work centers on 3D perception, mapping, and autonomous navigation. His key contributions lie at the intersection of computer vision and mobile robotics, particularly in developing methods for 3D geological modeling, Simultaneous Localization and Mapping (SLAM), and egomotion estimation. Viejo pioneered the use of Growing Neural Gas (GNG) networks to improve feature extraction and 3D reconstruction, enabling robust 6DoF motion estimation from generalized 3D data. His most cited work, "3D geological modelling using laser and hyperspectral data" (31 citations), introduced a framework for mobile robotic platforms to autonomously build 3D geological maps by fusing laser and hyperspectral data. He also contributed to efficient 3D data compression with the 3DCOMET dataset, addressing storage and computational limitations in robotics. Viejo's research has practical implications for field robotics, including planetary exploration and environmental monitoring, and his methods for combining visual features with neural networks have advanced the state of the art in real-time 3D SLAM.
Research Focus
Key Achievements
Top Papers
- 13D geological modelling using laser and hyperspectral data31 citations · 2010
- 2Using GNG to improve 3D feature extraction—Application to 6DoF egomotion25 citations · 2012
- 3
- 4A robust and fast method for 6DoF motion estimation from generalized 3D data16 citations · 2013
- 5A study of a soft computing based method for 3D scenario reconstruction7 citations · 2012
- 63DCOMET: 3D compression methods test dataset6 citations · 2015
- 7Active stereo based compact mapping4 citations · 2005
- 8Portable 3D laser-camera calibration system with color fusion for SLAM3 citations · 2013
- 9Using 3D GNG-based reconstruction for 6DoF egomotion2 citations · 2011