Dellaert Frank
Papers
1
Total Citations
2
H-Index
1
About
Frank Dellaert is a leading figure in robotics and computer vision, best known for pioneering work in probabilistic inference and factor graph-based optimization for state estimation. His research spans simultaneous localization and mapping (SLAM), structure from motion, and perception for autonomous systems, with a particular emphasis on robust, scalable algorithms. Dellaert’s most transformative contribution is the development of the Georgia Tech Smoothing and Mapping (GTSAM) library, a widely adopted open-source framework that has become a cornerstone for SLAM and sensor fusion research. His work on incremental smoothing and mapping (iSAM) revolutionized real-time, long-term autonomy by enabling efficient, incremental updates to factor graphs. With over 20,000 citations, his papers on iSAM and GTSAM are foundational in robotics. Notably, his 2017 paper on robotics for agricultural environments, though less cited, reflects his broader interest in deploying autonomous systems in unstructured, real-world settings. Dellaert’s achievements include being an IEEE Fellow and receiving the RSS Test of Time Award, cementing his legacy as a key architect of modern probabilistic robotics.
Research Focus
Key Achievements
Top Papers
- 1