Michael Himmelsbach

About

Michael Himmelsbach is a leading researcher in autonomous ground vehicle navigation, with a primary focus on LIDAR-based 3D perception, real-time point cloud segmentation, and off-road mobility. His most influential contribution is the development of a fast segmentation method for large-scale 3D point clouds, detailed in his highly cited 2010 paper (435 citations), which enables high-speed autonomous driving by processing long-range sensor data in real time. Himmelsbach also pioneered the "Driving with Tentacles" navigation architecture (134 citations), an integral structure for sensing and motion that was successfully demonstrated at both the C-Elrob 2007 and the DARPA Urban Challenge 2007, operating without GPS in unknown terrain. His work on real-time object classification using point feature histograms (130 citations) and comprehensive LIDAR-based 3D object perception systems (107 citations) has been instrumental in enabling safe urban navigation for autonomous vehicles like MuCAR-3. Additionally, Himmelsbach has contributed to multi-modal terrain mapping by fusing vision and LIDAR data, and to GIS-based topological localization for robust navigation in GPS-denied environments. With over 900 total citations, his research continues to shape the fields of autonomous mobility, sensor fusion, and real-time 3D perception.

Research Focus

Key Achievements

Top Papers

1. 1
   Fast segmentation of 3D point clouds for ground vehicles

   435 citations · 2010
2. 2
   Driving with tentacles: Integral structures for sensing and motion

   134 citations · 2008
3. 3
   Real-time object classification in 3D point clouds using point feature histograms

   130 citations · 2009
4. 4
   LIDAR-based 3D Object Perception

   107 citations · 2008
5. 5
   Autonomous Off-Road Navigation for MuCAR-3

   34 citations · 2011
6. 6
   GIS-based topological robot localization through LIDAR crossroad detection

   30 citations · 2011
7. 7
   Detection and tracking of road networks in rural terrain by fusing vision and LIDAR

   25 citations · 2011
8. 8
   Multi-modal local terrain maps from vision and LiDAR

   25 citations · 2017
9. 9
   Driving with Tentacles - Integral Structures for Sensing and Motion

   21 citations · 2009
10. 10
    A model-based object following system

    15 citations · 2009

Key Collaborators

Contact & Links