Michael Kaess
I am a postdoctoral associate with the Marine Robotics Group with John Leonard in the Computer Science and Artificial Intelligence Laboratory (CSAIL) at the Massachusetts Institute of Technology (MIT).My research focuses on probabilistic methods in mobile robotics and computer vision, in particular efficient localization and large-scale mapping, as well as data association.
I finished my PhD with Frank Dellaert at Georgia Tech in 2008. My thesis work focused on 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.
News
- August 2009: I attended ISRR in Lucerne, Switzerland.
- August 2009: My CVIU journal paper has been accepted.
- July 2009: My RAS journal paper has been accepted.
- June 2009: I had a joint workshop paper with Ed Olson on “Evaluating the Performance of Robot Mapping Systems” (pdf) at RSS in Seattle.
- May 2009: I attended ICRA in Kobe, Japan, and presented a joint paper with Kai Ni and Frank Dellaert on visual odometry.
Current Work
I work on ship hull inspection
with Franz
Hover in Ocean
Engineering, focusing on underwater localization and mapping with
the Hovering Autonomous Underwater Vehicle (HAUV). In addition to the
standard sensors of mobile robotics such as a gyro, the vehicle also
includes a doppler velocity log (DVL) and an imaging sonar.
I am also working with multiple students on dynamic and multi-robot mapping. And I am interested in porting my work to a quadrotor helicopter platform. More details soon...
For my previous work, please see my Georgia Tech web page.
Selected Publications
“Covariance Recovery from a Square Root Information Matrix for Data Association” by M. Kaess and F. Dellaert. Journal of Robotics and Autonomous Systems, 2009. In print. 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.
“Fast Incremental Square Root Information Smoothing” by M. Kaess, A. Ranganathan, and F. Dellaert. In Intl. Joint Conf. on Artificial Intelligence, IJCAI, (Hyderabad, India), Jan. 2007, pp. 2129-2134. Oral presentation acceptance ratio 15.7% (212 of 1353). 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.
Contact
Michael KaessMIT, CSAIL Room 230
32 Vassar St
Cambridge, MA 02139