AQUALOC: An Underwater Dataset for Visual-Inertial-Pressure Localization.

Maxime Ferrera (1, 2) , Vincent Creuze (2) , Julien Moras (1) , Pauline Trouvé-Peloux (1)

1 DTIS, ONERA, Université Paris Saclay (COmUE) [Palaiseau]
2 ICAR - Image & Interaction

We present a new dataset, dedicated to the development of simultaneous localization and mapping methods for underwater vehicles navigating close to the seabed. The data sequences composing this dataset are recorded in three different environments: a harbor at a depth of a few meters, a first archaeological site at a depth of 270 meters and a second site at a depth of 380 meters. The data acquisition is performed using Remotely Operated Vehicles equipped with a monocular monochromatic camera, a low-cost inertial measurement unit, a pressure sensor and a computing unit, all embedded in a single enclosure. The sensors' measurements are recorded synchronously on the computing unit and seventeen sequences have been created from all the acquired data. These sequences are made available in the form of ROS bags and as raw data. For each sequence, a trajectory has also been computed offline using a Structure-from-Motion library in order to allow the comparison with real-time localization methods. With the release of this dataset, we wish to provide data difficult to acquire and to encourage the development of vision-based localization methods dedicated to the underwater environment. The dataset can be downloaded from: http://www.lirmm.fr/aqualoc/

Mots clés: SLAM; Underwater robotics; Monocular Vision; IMU; Pressure; Dataset

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

Maxime Ferrera, Vincent Creuze, Julien Moras, Pauline Trouvé-Peloux. AQUALOC: An Underwater Dataset for Visual-Inertial-Pressure Localization.. The International Journal of Robotics Research, 2019, 38 (14), pp.1549-1559. ⟨10.1177/0278364919883346⟩. ⟨hal-02332498⟩