Thomas Schneider
Papers
8
Total Citations
736
H-Index
8
About
Thomas Schneider is a leading figure in visual-inertial robotics, whose work has fundamentally shaped how autonomous systems perceive and navigate the world. His research centers on robust state estimation, sensor calibration, and long-term mapping, with a particular focus on integrating multiple inertial measurement units (IMUs) for enhanced accuracy. Schneider's most influential contribution is the **maplab** open-source framework (270+ citations), which provides a complete ecosystem for visual-inertial mapping and localization, enabling drift-free pose estimation against prior maps—a cornerstone for reliable autonomous navigation. His work on **Kalibr** (379+ citations) extended the calibration toolbox to handle multiple IMUs and individual axes, solving a critical challenge for complex robotic systems. Beyond these foundational tools, Schneider has pioneered collaborative navigation for heterogeneous robot teams (e.g., flying and walking robots) and developed practical "teach and repeat" systems for aerial inspection, demonstrating how his research translates directly to industrial applications. With over 700 total citations, his contributions are essential reading for anyone working in visual-inertial SLAM, sensor fusion, or autonomous navigation.
Research Focus
Key Achievements
Top Papers
- 1
- 2
- 3Erasing bad memories: Agent-side summarization for long-term mapping24 citations · 2016
- 4Collaborative Navigation for Flying and Walking Robots18 citations · 2016
- 5Topomap: Topological Mapping and Navigation Based on Visual SLAM Maps15 citations · 2018
- 6
- 7Visual-Inertial Teach and Repeat for Aerial Inspection9 citations · 2018
- 8Visual-Inertial Teach and Repeat Powered by Google Tango8 citations · 2018