Position Correlated Vision Dataset from Multirotor and Fixed-wing sUAS of General Aviation aircraft, Fixed-wing sUAS, Multirotor sUAS, and Birds

Onboard autonomy is likely needed for safe autonomous Advanced Air Mobility (AAM) flight operations to keep aircraft from colliding with each other and buildings. Despite decades of Sense and Avoid research in the aviation domain, the specific requirements from an aviation authority for widespread autonomous operations for small aircraft and small Unmanned Aircraft Systems (sUAS) do not exist. AAM researchers need to provide reproducible methodologies for developing collision avoidance systems. One promising sensor for Sense and Avoid systems is the camera as it is lightweight, data rich, and can provide high angular resolution of objects. In this work, we present a vision dataset collected from multirotor sUAS and fixed-wing sUAS where the targets include multirotor sUAS, fixed-wing sUAS, General Aviation aircraft, and birds captured over seven days with varying flight conditions through thirty-one videos. A novel semi-autonomous data labeling technique is presented utilizing an object detection and tracking pipeline. The dataset includes onboard flight controller logs along with the computed separation distance between the ownship and intruder aircraft per frame.

This data provides anticipated benefit for development efforts targeting sensor fusion, image processing & computer vision, and autonomy technology. This data is anticipated to benefit the development or maturation of technical methods for sense and avoid.

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