Papers
8
Total Citations
234
H-Index
8
About
Elena Stumm is a leading researcher in robotics and autonomous navigation, whose work has fundamentally advanced visual place recognition and collaborative localization. Her research centers on developing robust perception systems that enable robots to navigate complex environments with precision. Stumm’s most influential contribution is her pioneering approach to place recognition, a critical component of SLAM (Simultaneous Localization and Mapping) that prevents positional drift. Her 2016 paper on point cloud descriptors for place recognition, with 52 citations, introduced sparse visual information techniques that have become foundational in the field. She further advanced the discipline by establishing probabilistic place recognition using covisibility maps (40 citations), which diminished the influence of pose choice during mapping. Stumm’s innovative work extends to multi-robot systems, where she developed collaborative localization methods for aerial and ground robots (29 citations), exploiting complementary capabilities to improve situational awareness. Her research on tensor-voting-based navigation for 3D surface inspection (27 citations) and agent-side summarization for long-term mapping (24 citations) demonstrates her versatility in addressing real-world robotic challenges. Stumm’s contributions have been instrumental in pushing the boundaries of robotic autonomy, making her a respected figure in the field of visual navigation and mapping.
Research Focus
Key Achievements
Top Papers
- 1
- 2Probabilistic place recognition with covisibility maps40 citations · 2013
- 3Location graphs for visual place recognition30 citations · 2015
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- 5
- 6Erasing bad memories: Agent-side summarization for long-term mapping24 citations · 2016
- 7
- 8Map quality evaluation for visual localization12 citations · 2017