Marko Thiel
Papers
6
Total Citations
71
H-Index
4
About
Marko Thiel is a robotics researcher whose work sits at the intersection of robot perception, 3D object detection, and safety-critical autonomous systems. His research addresses a fundamental challenge in the field: bridging the gap between autonomous driving technology and real-world mobile robotics deployments in diverse, dynamic environments. Thiel's most prominent contribution is his involvement in the MCD (Multi-Campus Dataset) project, a large-scale, diverse dataset designed to push the boundaries of robot perception beyond the autonomous driving domain — a paper that has already garnered 48 citations since its 2024 publication, signaling strong community interest. Complementing this, his work on UADA3D tackles unsupervised adversarial domain adaptation for 3D object detection using sparse LiDAR data, addressing scenarios with large domain gaps that prior methods neglected. Beyond perception, Thiel has made meaningful contributions to safety-critical robot control architectures, proposing microservice-based frameworks that enable more sophisticated situational assessment for mobile robots operating in public spaces. His 2023 survey bridging self-driving car detection methods and mobile robotics further demonstrates his commitment to making cutting-edge perception accessible across platforms. Collectively, his research positions him as an emerging voice in robust, transferable robot autonomy.
Research Focus
Key Achievements
Top Papers
- 1MCD: Diverse Large-Scale Multi-Campus Dataset for Robot Perception48 citations · 2024
- 2
- 3
- 4MCD: Diverse Large-Scale Multi-Campus Dataset for Robot Perception4 citations · 2024
- 5
- 6