General Information
Details
Professor Dellaert’s research focuses on large-scale inference for autonomous robot systems, on land, air, and in water. He pioneered the use of several probabilistic methods in both computer vision and robotics. With Dieter Fox and Sebastian Thrun, he has introduced the Monte Carlo localization method for estimating and tracking the pose of robots, which is now a standard and popular tool in mobile robotics. More recently, he has investigated 3D reconstruction in large-scale environments by taking a graph-theoretic view and introduced factor graphs into the mainstream language of the robotics community.
Professor Dellaert’s teaching is focused on the fundamental of robotics, especially state estimation, perception, and model-predictive control. He teaches classes at both the undergraduate and graduate levels, including the main introductory robotics class on campus, CS 3630, a cross-listed class on advanced mobile robotics, and computer vision. Professor Dellaert’s research is student-focused and actively involves both graduate and undergraduate students.
Books
Introduction to Robotics and Perception, Frank Dellaert and Seth Hutchinson, in preparation with Cambridge University Press. Online version at https://www.roboticsbook.org
SLAM Handbook, three edited volumes, with Luca Carlone, Ayoung Kim, Frank Dellaert Timothy Barfoot, and Daniel Cremers, editors. in preparation with Cambridge University Press
Preview: https://github.com/SLAM-Handbook-contributors/slam-handbook-public-release
Selected Papers
Yetong Zhang et al, Constraint Manifolds for Robotic Inference and Planning,
IEEE International Conference on Robotics and Automation (ICRA), May 29 - June 2, 2023
Continuous-time Gaussian process motion planning via probabilistic inference, Mustafa Mukadam, Jing Dong, Xinyan Yan, Frank Dellaert, and Byron Boots, International Journal of Robotics Research, 2018. Best IJRR Paper of 2018 Award.
Factor graphs for robot perception, Frank Dellaert and Michael Kaess, Foundations and Trends in Robotics, 2017. Translated in Mandarin.
On-Manifold Preintegration for Real-Time Visual-Inertial Odometry, Christian Forster, Luca Carlone, Frank Dellaert, and Davide Scaramuzza, IEEE Transactions on Robotics, 2017.
King-Sun Fu Transactions on Robotics Best paper award for 2017.
iSAM2: Incremental Smoothing and Mapping Using the Bayes Tree, Michael Kaess, Hordur Johannsson, Richard Roberts, Viorela Ila, John Leonard, and Frank Dellaert, International Journal of Robotics Research, 2012
Square Root SAM: Simultaneous Location and Mapping via Square Root Information Smoothing, F. Dellaert and M. Kaess, International Journal on Robotics Research (IJRR), 2006. Robotics: Science and Systems (RSS) 2020 Test of Time Award.
Monte Carlo Localization for Mobile Robots, Frank Dellaert, Dieter Fox, Wolfram Burgard, and Sebastian Thrun, IEEE International Conference on Robotics and Automation (ICRA), 1999. ICRA Milestone Paper Award, awarded in 2020, for the most influential ICRA paper in the years 1998-2001.
Monte Carlo Localization -- Efficient position estimation for mobile robots, Dieter Fox, Wolfram Burgard, Frank Dellaert, and Sebastian Thrun, National Conference on Artificial Intelligence (AAAI), 1999. AAAI Classic Paper award, awarded in 2018