Regular biography
Michael Kaess is an Associate Professor in the Department of Mechanical Engineering at Carnegie Mellon University. His research interests include mobile robot autonomy, with a focus on efficient inference at the intersection of linear algebra and probabilistic graphical models for 3D mapping and localization. Kaess previously served as a research scientist and postdoctoral associate at MIT's Marine Robotics Lab under John Leonard. He earned his Ph.D. in Computer Science from Georgia Institute of Technology in 2008, advised by Frank Dellaert. His contact information is available at kaess@cmu.edu.
Scholar-generated biography
Michael Kaess is an associate professor at Carnegie Mellon University, specializing in Robotics, Computer Vision, and related fields. His research focuses on SLAM (Simultaneous Localization and Mapping), 3D Reconstruction, and State Estimation, with an emphasis on improving the accuracy and efficiency of robotic perception and navigation. His work includes incremental smoothing and mapping techniques, factor graphs for robot perception, and real-time dense RGB-D SLAM with volumetric fusion. He has also explored applications in underwater navigation and cooperative mapping, demonstrating his commitment to advancing autonomous systems through robust state estimation and information fusion.