Simultaneous Localization and Mapping
One of my secret weapons is possessing expertise in both 3D modeling in the field of computer vision and the simultaneous localization and mapping (SLAM) problem in robotics,
two problems that share a similar mathematical formulation. I exploited this in my work on linear SLAM and Intrinsic Localization and Mapping (ILM), both advancing the state of the art in
SLAM using computer vision style algorithms. My work on MCMC sampling over large discrete spaces also led to the development of a wholly new concept in SLAM: probabilistic topological maps.
This very recent work enables one to build a probability distribution over topological maps rather than detailed metric maps as have been more popular. By sampling over topological maps to
represent the uncertainty over them we combine the advantages of both metric maps (a sound probabilistic basis) and topological maps (scalability to large environments) in one
representation. Although the space of topological maps is combinatorially large, MCMC sampling can still enable one to perform inference in these large spaces.
- Bayesian Inference in the Space of Topological Maps, Ananth Ranganathan, Emanuele Menegatti, and Frank Dellaert, IEEE Transactions on Robotics, Accepted for publication, to appear in 2005
- Square Root SAM, Frank Dellaert, Robotics: Science and Systems 2005 , To appear: will be available
as TR soon.
- Data driven MCMC for Appearance-based Topological Mapping, Ananth Ranganathan and Frank
Dellaert, Robotics: Science and Systems 2005 , To appear: will be available as TR soon.
- A Markov Chain Monte Carlo Approach to Closing the Loop in SLAM, Michael Kaess and Frank Dellaert, ICRA 2005, Barcelona, Spain
- Using Hierarchical EM to Extract Planes from 3D Range Scans. , Rudolph Triebel, Wolfram Burgard and Frank Dellaert, ICRA 2005, Barcelona, Spain
- Inference In The Space Of Topological Maps: An MCMC-based Approach
Ananth Ranganathan and Frank Dellaert
IROS 2004, Sendai, Japan
- Intrinsic Localization and Mapping with 2 Applications: Diffusion Mapping and Marco Polo Localization, ICRA
- Marco Polo Localization,, ICRA 03
- The Georgia Tech Yellow Jackets: A Marsupial Team for Urban Search and Rescue, AAAI USAR Workshop, 2002
- Linear 2D Localization and Mapping for Single and Multiple Robots,, ICRA 02
- Mosaicing a Large Number of Widely Dispersed, Noisy, and Distorted Images: A Bayesian Approach, CMU TR 1999
Frank Dellaert's Homepage