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For real-world applications, autonomous mobile robotic platforms must becapable of navigating safely in a multitude of different and dynamicenvironments with accurate and robust localization being a key prerequisite. Tosupport further research in this domain, we present the INSANE data sets - acollection of versatile Micro Aerial Vehicle (MAV) data sets forcross-environment localization. The data sets provide various scenarios withmultiple stages of difficulty for localization methods. These scenarios rangefrom trajectories in the controlled environment of an indoor motion capturefacility, to experiments where the vehicle performs an outdoor maneuver andtransitions into a building, requiring changes of sensor modalities, up topurely outdoor flight maneuvers in a challenging Mars analog environment tosimulate scenarios which current and future Mars helicopters would need toperform. The presented work aims to provide data that reflects real-worldscenarios and sensor effects. The extensive sensor suite includes varioussensor categories, including multiple Inertial Measurement Units (IMUs) andcameras. Sensor data is made available as raw measurements and each data setprovides highly accurate ground truth, including the outdoor experiments wherea dual Real-Time Kinematic (RTK) Global Navigation Satellite System (GNSS)setup provides sub-degree and centimeter accuracy (1-sigma). The sensor suitealso includes a dedicated high-rate IMU to capture all the vibration dynamicsof the vehicle during flight to support research on novel machinelearning-based sensor signal enhancement methods for improved localization. Thedata sets and post-processing tools are available at:https://sst.aau.at/cns/datasets

INSANE: Cross-Domain UAV Data Sets with Increased Number of Sensors for developing Advanced and Novel Estimators