Axel Rottmann

About

Axel Rottmann is a robotics researcher whose work spans two interconnected frontiers: semantic environment understanding for mobile robots and autonomous control of aerial platforms, particularly indoor blimps. His most influential contributions lie in teaching robots to recognize and label meaningful places in their surroundings — distinguishing kitchens from offices, corridors from seminar rooms — using data from laser, vision, and other sensors. His 2006 paper on supervised semantic place labeling has accumulated 180 citations, establishing him as a key figure in the field, while his complementary work applying boosting algorithms such as AdaBoost to indoor semantic classification further cemented this reputation with nearly 100 additional citations. Beyond place recognition, Rottmann made significant strides in autonomous blimp research, developing lightweight embedded platforms weighing under 200 grams and pioneering model-free reinforcement learning approaches for continuous blimp height control. His work on adaptive autonomous control using Gaussian processes and online value iteration reflects a broader commitment to learning-based robot control that avoids brittle hand-tuned models. He also contributed to 3D mapping using low-cost vision and inertial sensors — tools suited precisely to the payload-constrained aerial systems he championed. Collectively, his research bridges perception, semantic reasoning, and adaptive control in real-world robotic systems.

Research Focus

Key Achievements

Top Papers

1. 1
   Supervised semantic labeling of places using information extracted from sensor data

   180 citations · 2006
2. 2
   Semantic place classification of indoor environments with mobile robots using boosting

   99 citations · 2005
3. 3
   Autonomous blimp control using model-free reinforcement learning in a continuous state and action space

   39 citations · 2007
4. 4
   Semantic labeling of places using information extracted from laser and vision sensor data

   36 citations · 2006
5. 5
   Semantic labeling of places

   30 citations · 2005
6. 6
   Learning maps in 3D using attitude and noisy vision sensors

   30 citations · 2007
7. 7
   A probabilistic sonar sensor model for robust localization of a small-size blimp in indoor environments using a particle filter

   28 citations · 2009
8. 8
   Adaptive autonomous control using online value iteration with gaussian processes

   23 citations · 2009
9. 9
   Using AdaBoost for Place Labeling and Topological Map Building

   16 citations · 2007
10. 10
    Towards an Experimental Autonomous Blimp Platform.

    15 citations · 2007

Key Collaborators

Contact & Links