Astrobee ISS Free-Flyer Datasets for Space Intra-Vehicular Robot Navigation Research

Suyoung Kang, Ryan Soussan, Daekyeong Lee, Brian Coltin, Andres Mora Vargas, Marina Moreira, Katie Browne, Ruben Garcia, Maria Bualat, Trey Smith, Jonathan Barlow, Jose Benavides, Eunju Jeong, Pyojin Kim

Abstract: We present the first annotated benchmark datasets for evaluating free-flyer visual-inertial localization and mapping algorithms in a zero-g spacecraft interior. The Astrobee free-flying robots that operate inside the International Space Station (ISS) collected the datasets. Space intra-vehicular free-flyers face unique localization challenges: their IMU does not provide a gravity vector, their attitude is fully arbitrary, and they operate in a dynamic, cluttered environment. We extensively evaluate state-of-the-art visual navigation algorithms on these challenging Astrobee datasets, showing superior performance of classical geometry-based methods over recent data-driven approaches. The datasets include monocular images and IMU measurements, with multiple sequences performing a variety of maneuvers and covering four ISS modules. The sensor data is spatio-temporally aligned, and extrinsic/intrinsic calibrations, ground-truth 6-DoF camera poses, and detailed 3D CAD models are included to support evaluation. The datasets are available at: https://astrobee-iss-dataset.github.io/.

External IDs:dblp:journals/ral/KangSLCVMBGBSBBJK24