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Dr. Michael Kaess

Assistant Research Professor at CMU
please see my CMU page for up-to-date information

Computer Science and Artificial Intelligence Lab (CSAIL)
Massachusetts Institute of Technology (MIT)

32 Vassar St., Room 232
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 was a member of the Marine Robotics Lab of John Leonard at MIT. 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 my projects:

Kintinuous: Live dense 3D modeling

Building-scale mapping and long-term mapping

Ship hull inspection: Closed loop control of underwater robot

Highlights   [All News...]

Selected Publications   [All Publications...]

“Deformation-based Loop Closure for Large Scale Dense RGB-D SLAM” by T. Whelan, M. Kaess, J.J. Leonard, and J.B. McDonald. In IEEE/RSJ Intl. Conf. on Intelligent Robots and Systems, IROS, (Tokyo, Japan), Nov 2013. Details. Download: PDF.

“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.

“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.

Last updated: Nov 4, 2013