AirMuseum: a heterogeneous multi-robot dataset for stereo-visual and inertial Simultaneous Localization And Mapping

Rodolphe Dubois (1, 2, 3) , Alexandre Eudes (1) , Vincent Frémont (4, 2, 3)

1 DTIS, ONERA, Université Paris Saclay [Palaiseau]
2 LS2N - équipe ARMEN - Autonomie des Robots et Maîtrise des interactions avec l’ENvironnement
3 LS2N - Laboratoire des Sciences du Numérique de Nantes
4 ECN - École Centrale de Nantes

This paper introduces a new dataset dedicated to multi-robot stereo-visual and inertial Simultaneous Localization And Mapping (SLAM). This dataset consists in five indoor multi-robot scenarios acquired with ground and aerial robots in a former Air Museum at ONERA Meudon, France. Those scenarios were designed to exhibit some specific opportunities and challenges associated to collaborative SLAM. Each scenario includes synchronized sequences between multiple robots with stereo images and inertial measurements. They also exhibit explicit direct interactions between robots through the detection of mounted AprilTag markers [1]. Ground-truth trajectories for each robot were computed using Structure-from-Motion algorithms and constrained with the detection of fixed AprilTag markers placed as beacons on the experimental area. Those scenarios have been benchmarked on state-of-the-art monocu-lar, stereo and visual-inertial SLAM algorithms to provide a baseline of the single-robot performances to be enhanced in collaborative frameworks.

Mots clés: Robotics; Visual SLAM; Multi-Robot SLAM

Domaines: Robotique [cs.RO]; Vision par ordinateur et reconnaissance de formes [cs.CV]

Rodolphe Dubois, Alexandre Eudes, Vincent Frémont. AirMuseum: a heterogeneous multi-robot dataset for stereo-visual and inertial Simultaneous Localization And Mapping. 2020 IEEE International Conference on Multisensor Fusion and Integration (MFI 2020), Sep 2020, Karlsruhe, Germany. ⟨10.1109/MFI49285.2020.9235257⟩. ⟨hal-02943634⟩