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
“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.