Daniel Maier
Papers
8
Total Citations
344
H-Index
7
About
Daniel Maier is a leading researcher in autonomous robot navigation, with a particular focus on humanoid robots operating in complex, unstructured environments. His work bridges perception, localization, and motion planning, enabling robots to navigate 3D spaces using only onboard sensors. Maier’s most influential contributions include real-time navigation systems based on depth camera data (96 citations), improved GPS models for urban terrain (84 citations), and integrated perception and footstep planning for humanoids (48 citations). He pioneered probabilistic localization methods for humanoid robots in indoor environments (43 citations) and developed self-supervised obstacle detection techniques using monocular vision and sparse laser data (33 citations). His work on the Nao humanoid platform has been particularly impactful, demonstrating collision-free navigation in cluttered settings. Beyond navigation, Maier has explored multimodal human-robot interaction, enabling humanoids to play musical instruments using visual and auditory feedback. With over 340 total citations across his key publications, Maier’s research has advanced the state of the art in autonomous navigation, making humanoid robots more capable of operating safely and efficiently in real-world environments.
Research Focus
Key Achievements
Top Papers
- 1Real-time navigation in 3D environments based on depth camera data96 citations · 2012
- 2Improved GPS sensor model for mobile robots in urban terrain84 citations · 2010
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- 6VISION-BASED HUMANOID NAVIGATION USING SELF-SUPERVISED OBSTACLE DETECTION26 citations · 2013
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- 8Using visual and auditory feedback for instrument-playing humanoids2 citations · 2014