Datasets of Long Range Navigation Experiments in a Moon Analogue Environment on Mount Etna
Konferenz: ISR 2018 - 50th International Symposium on Robotics
20.06.2018 - 21.06.2016 in München, Germany
Tagungsband: ISR 2018
Seiten: 7Sprache: EnglischTyp: PDFPersönliche VDE-Mitglieder erhalten auf diesen Artikel 10% Rabatt
Vayugundla, Mallikarjuna; Steidle, Florian; Smisek, Michal; Schuster, Martin J.; Bussmann, Kristin; Wedler, Armin (German Aerospace Center (DLR), Robotics and Mechatronics Center (RMC), Münchner Str. 20, 82234 Wessling, Germany)
Long range navigation capabilities are crucial to increase the level of autonomy for robotic planetary exploration missions. As the opportunities to collect data on the surfaces of other planets are both very limited and expensive, space analogue sites on Earth play an important role to develop and test robotic systems. We provide and present two datasets captured with our Lightweight Rover Unit (LRU) at a planetary surface analogue test site on Mt. Etna, Sicily, Italy. In distinction to many other robot navigation datasets, we were able to capture datasets in an environment that is in terms of its visual and terramechanical properties close to the character of surfaces of rocky planets, hence making our data valuable for the development of visual-inertial navigation systems for planetary and unstructured GPS-denied outdoor environments. We make both of our datasets publicly available and free to download for other researchers to use them to test, improve and evaluate their navigation methods. We provide raw data in the form of ROS bagfiles containing gray-scale images, dense depth images, sensor readings from an Inertial Measurement Unit (IMU) and wheel odometry estimates. In addition, the data contains ground truth for the rover trajectory obtained via differential GPS (DGPS) to allow an evaluation of robot localization methods. The datasets were recorded during experiments, in which our rover traversed paths of approximately 1 km in length each. This makes them useful for testing pose estimation methods over long ranges.