Papers
24
Total Citations
840
H-Index
12
About
Peer Neubert is a robotics researcher whose work centers on long-term autonomous navigation, visual place recognition (VPR), and robust robot localization in dynamically changing environments. His most influential contributions address one of mobile robotics' most persistent challenges: enabling robots to reliably recognize places despite dramatic appearance changes caused by shifting seasons, lighting conditions, and weather. His landmark 2013 paper challenging the SeqSLAM algorithm across a 3,000 km, four-season journey (249 citations) became a widely referenced benchmark in the field, while companion work on appearance change prediction (109 citations) further established his reputation for pushing long-term navigation research. Neubert has also made notable contributions to hyperdimensional computing for robotics (102 citations), introducing neurologically inspired computational frameworks to the field. His 2023 VPR tutorial (53 citations) reflects his commitment to accessible, foundational scholarship for emerging researchers. Spanning UAV corridor flight, 3D map-based navigation, and vector semantic descriptors, his diverse portfolio demonstrates a sustained effort to bridge theoretical innovation with real-world robotic deployment, making him a significant and practical voice in autonomous systems research.
Research Focus
Key Achievements
Top Papers
- 1Are We There Yet? Challenging SeqSLAM on a 3000 km Journey Across All Four Seasons249 citations · 2013
- 2Appearance change prediction for long-term navigation across seasons109 citations · 2013
- 3An Introduction to Hyperdimensional Computing for Robotics102 citations · 2019
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- 5Visual Place Recognition: A Tutorial [Tutorial]53 citations · 2023
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- 10Vector Semantic Representations as Descriptors for Visual Place Recognition28 citations · 2021