Michael Kaess
Papers
12
Total Citations
238
H-Index
7
About
Michael Kaess is a prominent robotics researcher whose work spans simultaneous localization and mapping (SLAM), state estimation, sensor fusion, and autonomous robot perception. Based at Carnegie Mellon University, Kaess has made significant contributions to enabling robots to navigate, map, and understand complex environments using diverse sensing modalities. His research on dense RGB-D mapping, exemplified by extensions to the Kintinuous system, helped advance real-time 3D scene reconstruction, while his ARM-SLAM framework tackled the challenging problem of joint estimation of robot kinematics and environmental structure. Kaess has pushed the boundaries of multi-modal sensing, exploring unconventional modalities such as tactile sensors for shape mapping, ground-penetrating radar for subsurface localization, millimeter-wave radar for all-weather odometry, and sonar for underwater occupancy mapping. His work on distributed and asynchronous SLAM algorithms addresses the growing need for scalable multi-robot coordination. His contributions to subterranean exploration robotics, garnering 50 citations, reflect real-world impact in high-stakes autonomous systems. With papers consistently attracting meaningful citations across a broad research portfolio, Kaess stands as a versatile and influential figure shaping the future of robust, perception-driven robotics.
Research Focus
Key Achievements
Top Papers
- 1ShapeMap 3-D: Efficient shape mapping through dense touch and vision50 citations · 2022
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- 3Robust Tracking for Real-Time Dense RGB-D Mapping with Kintinuous47 citations · 2012
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