3.5k citations · 6k across the 59 of their papers we have counts for
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From Coarse to Fine: Robust Hierarchical Localization at Large Scale
Paul-Edouard Sarlin, Cesar Cadena, Roland Siegwart +1
Robust and accurate visual localization is a fundamental capability for numerous applications, such as autonomous driving, mobile robotics, or augmented reality. It remains, howeve…
Leveraging Deep Visual Descriptors for Hierarchical Efficient Localization
Paul-Edouard Sarlin, Frédéric Debraine, Marcin Dymczyk +2
Many robotics applications require precise pose estimates despite operating in large and changing environments. This can be addressed by visual localization, using a pre-computed 3…
Empty Cities: Image Inpainting for a Dynamic-Object-Invariant Space
Berta Bescos, José Neira, Roland Siegwart +1
In this paper we present an end-to-end deep learning framework to turn images that show dynamic content, such as vehicles or pedestrians, into realistic static frames. This objecti…
Map Management for Efficient Long-Term Visual Localization in Outdoor Environments
Mathias Bürki, Marcin Dymczyk, Igor Gilitschenski +3
We present a complete map management process for a visual localization system designed for multi-vehicle long- term operations in resource constrained outdoor environments. Outdoor…
Modular Sensor Fusion for Semantic Segmentation
Hermann Blum, Abel Gawel, Roland Siegwart +1
Sensor fusion is a fundamental process in robotic systems as it extends the perceptual range and increases robustness in real-world operations. Current multi-sensor deep learning b…
Aerial-Ground collaborative sensing: Third-Person view for teleoperation
Abel Gawel, Yukai Lin, Théodore Koutros +2
Rapid deployment and operation are key requirements in time critical application, such as Search and Rescue (SaR). Efficiently teleoperated ground robots can support first-responde…