Papers
11
Total Citations
561
H-Index
8
About
Lukas Schmid is a robotics researcher specializing in autonomous robot perception, exploration planning, and dynamic environment understanding. His work sits at the intersection of informative path planning, metric-semantic mapping, and robot autonomy, with a focus on enabling robots to operate reliably in complex, real-world settings. Schmid's most influential contribution, "An Efficient Sampling-Based Method for Online Informative Path Planning" (2020, 297 citations), addressed critical limitations of local minima in sampling-based planners, significantly advancing the state of autonomous exploration. Building on this foundation, he has developed systems for handling dynamic environments, including *Dynablox* (67 citations), a real-time moving object detection framework, and *Khronos*, a unified spatio-temporal SLAM approach for environments that change over time. His *Clio* system (2024) pushes boundaries further by enabling open-set, task-driven 3D scene graphs using modern vision-language models. Beyond core robotics, Schmid has demonstrated versatile impact through cross-domain work, including a spatio-temporal framework for plant stress phenotyping (50 citations). His research on distribution learning for exploration planning and cooperative multi-robot navigation for planetary missions reflects a consistent drive toward efficient, scalable autonomy. With over 550 cumulative citations, Schmid's work is shaping the future of intelligent robotic systems.
Research Focus
Key Achievements
Top Papers
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- 3A spatio temporal spectral framework for plant stress phenotyping50 citations · 2019
- 4<i>Clio:</i> Real-Time Task-Driven Open-Set 3D Scene Graphs35 citations · 2024
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