You can download the whole or a portion of the ALFA dataset from here (alternative location here). Please contact us if you encounter issues or to ask additional questions. Scherer, “Automatic Real-time Anomaly Detection for Autonomous Aerial Vehicles,” in 2019 IEEE International Conference on Robotics and Automation (ICRA), May 2019, pp.5679-5685. Left stuck at zero, then only right stuck at zeroĪ. The list of the sequences is as followed: The main focus of ALFA dataset is the processed data sequences. Please proceed to the citation section for more details. Two publications provide the description of the dataset (including the structure, data collection, etc.). Please refer to the Download section below to download the dataset and the code. More information about the project is available on the project page (link). The dataset was collected during our research on a novel real-time anomaly detection method for autonomous aerial vehicles. The codes are written independent of ROS or any other external library and are cross-platform (tested in Linux, Mac OS and Windows). A supplemental code is provided for working with the dataset in C++, Python and MATLAB. Dataflash Logs: The logs recorded on the Pixhawk during the flights. Telemetry Logs: The telemetry logs from the onboard NVidia TX2 computer connected to the Pixhawk autopilot. These files are recorded using the modified mavros ROS package connected to the Pixhawk running the modified Ardupilot 3.9.0beta1 through MAVLink protocol. It contains both the manual and autonomous flight sequences collected during the flights, without any processing. bag files are recorded using the modified mavros ROS package connected to the Pixhawk running the modified Ardupilot 3.9.0beta1 through MAVLink 2.0 protocol. The files include the ground truth of the type and the time of the failure and are provided in the ROS. Processed Data: 47 sequences of fully autonomous flight sequences with eight different types of faults happening during the flights. The AirLab Failure and Anomaly ( ALFA) Dataset includes the data collected from tens of autonomous flights for failure detection and anomaly detection research.
Azarakhsh Keipour AirLab Failure and Anomaly (ALFA) Dataset
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