Kevin Daun
Papers
13
Total Citations
139
H-Index
7
About
Kevin Daun is a leading researcher in rescue robotics, specializing in autonomous systems for disaster response. His work focuses on developing robust robotic solutions for urban search and rescue (USAR), with key contributions in simultaneous localization and mapping (SLAM), multi-sensor fusion, and autonomous navigation in degraded environments. Daun’s research has produced innovative methods like HectorGrapher, a continuous-time LiDAR SLAM system using multi-resolution signed distance functions for challenging terrain, and large-scale 2D laser SLAM with truncated signed distance functions, each garnering over 12 citations. He played a pivotal role in the German Rescue Robotics Center (DRZ), where his holistic approach to robotic systems for emergency response has been cited 36 times, and his lessons from robot-assisted disaster deployments (23 citations) provide critical insights for real-world missions. Daun also explores advanced topics like affordance-based semantic mapping, radiation mapping with Gaussian processes, and robust policy learning for robot swarms. His work on reliable autonomy for rescue robots, with over 11 citations, underscores his commitment to enhancing operator support and system dependability. With a portfolio spanning SLAM, autonomy, and human-robot interaction, Daun’s research directly impacts first responders, making him a key figure in advancing robotic assistance for life-saving operations.
Research Focus
Key Achievements
Top Papers
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- 3Learning Robust Policies for Object Manipulation with Robot Swarms15 citations · 2018
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- 5Large Scale 2D Laser SLAM using Truncated Signed Distance Functions12 citations · 2019
- 6Towards Highly Reliable Autonomy for Urban Search and Rescue Robots11 citations · 2015
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