Freetures: Localization in Signed Distance Function Maps

• Published in: IEEE robotics and automation letters 2024, p.1-1
• Main Authors: Millane, Alexander James, Oleynikova, Helen, Lanegger, Christian, Delmerico, Jeffrey, Nieto, Juan, Siegwart, Roland, Pollefeys, Marc, Cadena Lerma, Cesar
• Format: Article
• Language: English
• Subjects: Aerial Systems: Perception and Autonomy; Estimation; Feature extraction; Geometry; Laser radar; Localization; Location awareness; SLAM; Three-dimensional displays; Two dimensional displays
• ISSN: 2377-3766; 2377-3766
• DOI: 10.1109/LRA.2021.3052388

Localization of a robotic system within a previously mapped environment is important for reducing estimation drift and for reusing previously built maps. Existing techniques for geometry-based localization have focused on the description of local surface geometry, usually using pointclouds as the underlying representation. We propose a system for geometrybased localization that extracts features directly from an implicit surface representation: the Signed Distance Function (SDF). The SDF varies continuously through space, which allows the proposed system to extract and utilize features describing both surfaces and free-space. Through evaluations on public datasets, we demonstrate the flexibility of this approach, and show an increase in localization performance over state-of-the-art handcrafted surfaces-only descriptors. We achieve an average improvement of ~12% on an RGB-D dataset and ~18% on a LiDAR-based dataset. Finally, we demonstrate our system for localizing a LiDAR-equipped Micro Aerial Vehicle (MAV) within a previously built map of a search and rescue training ground. Our system, called freetures, is available as open source ^{1} .