Research Scientist [Curriculum Vitae]
Computer Science and Artificial Intelligence Lab (CSAIL)
Massachusetts Institute of Technology (MIT)
32 Vassar St., Room 230
Cambridge, MA 02139
Phone: +1 617 452 2852
My research focuses on probabilistic methods in mobile robotics and
computer vision. I am working on inference with large sparse matrices
and graphical models, in particular exploring their
connections. Solutions to this problem are of interest for robot
localization and large-scale mapping. One particular application that
I have been working on is the navigation for closed loop control of an
autonomous underwater vehicle to inspect the hulls of large ships.
I am a member of
the Marine Robotics
Lab of John
Leonard. I finished my PhD
with Frank Dellaert
at Georgia Tech in 2008.
My dissertation introduced an
efficient algorithm called iSAM for simultaneous localization and
mapping (SLAM), which is the problem of mapping a previously unknown
environment while at the same time using this map for localization.
Mapping has many important applications, including inspection of
underwater structures, indoor navigation for service robots and
search-and-rescue scenarios, navigation for autonomous cars and space
applications. Here are some recent highlights from
Jan 2011: Check out our new iSAM2paper that will appear in ICRA 2011 in Shanghai. I also co-authored a paper with Maurice Fallon that uses iSAM to fuse sonar data for AUV navigation (nominated for best automation paper).
Dec 2010: I presented our Bayes tree work at WAFR 2010, and also gave a related talk at the National University of Singapore.
Aug 2010: Check out my open source code for iSAM, now available here under the LGPL license. Includes full documentation, a few datasets and a 3D viewer.
“An Incremental Trust-Region Method for Robust Online Sparse Least-Squares Estimation” by D.M. Rosen, M. Kaess, and J.J. Leonard. In IEEE Intl. Conf. on Robotics and Automation, ICRA, (St. Paul, MN), May 2012. Details. Download: PDF.
“iSAM2: Incremental Smoothing and Mapping Using the Bayes Tree” by M. Kaess, H. Johannsson, R. Roberts, V. Ila, J.J. Leonard, and F. Dellaert. Intl. J. of Robotics Research, IJRR, vol. 31, Feb. 2012, pp. 217-236. Details. Download: PDF.
“The Bayes Tree: An Algorithmic Foundation for Probabilistic Robot Mapping” by M. Kaess, V. Ila, R. Roberts, and F. Dellaert. In Intl. Workshop on the Algorithmic Foundations of Robotics, WAFR, (Singapore), Dec. 2010. Details. Download: PDF.
“Multiple Relative Pose Graphs for Robust Cooperative Mapping” by B. Kim, M. Kaess, L. Fletcher, J.J. Leonard, A. Bachrach, N. Roy, and S. Teller. In IEEE Intl. Conf. on Robotics and Automation, ICRA, (Anchorage, Alaska), May 2010, pp. 3185-3192. Details. Download: PDF.
“Covariance Recovery from a Square Root Information Matrix for Data Association” by M. Kaess and F. Dellaert. Journal of Robotics and Autonomous Systems, vol. 57, Dec. 2009, pp. 1198-1210. Details. Download: PDF.
“iSAM: Incremental Smoothing and Mapping” by M. Kaess, A. Ranganathan, and F. Dellaert. IEEE Trans. on Robotics, vol. 24, no. 6, Dec. 2008, pp. 1365-1378. Details. Download: PDF.
“Square Root SAM: Simultaneous Localization and Mapping via Square Root Information Smoothing” by F. Dellaert and M. Kaess. Intl. J. of Robotics Research, vol. 25, no. 12, Dec. 2006, pp. 1181-1204. Details. Download: PDF.
“MCMC-based Multiview Reconstruction of Piecewise Smooth Subdivision Curves with a Variable Number of Control Points” by M. Kaess, R. Zboinski, and F. Dellaert. In Eur. Conf. on Computer Vision, ECCV, (Prague, Czech Republic), May 2004, pp. 329-341. Acceptance ratio 34.2% (190 of 555). Details. Download: PDF.