Spatiotemporal Maneuverability Hazard Analytics from Low-Altitude UAS Sensors

Low-altitude unmanned aerial systems (UAS) have a rapidly changing field of view for most sensor payloads, especially downward-looking high-resolution visual imagery. In this research, we explore visible-spectrum derived spatiotemporal awareness applied to companion sensor phenomenologies and computationally derived information, such as computer vision- based road extraction and maneuverability hazard localization. Specifically, short-span visual-temporal analysis is performed to understand the platform motion and therefore geospatial context of measurements, whether from sensors or analytical processes. The mathematical model of frame-to-frame platform motion is extracted as homographies and affine transformations that describe how one image "fits into" the next. By coupling these computed motion models with derived data, such as road segmentation and object localization, we show how this technique allows the development of temporal-confidence to aid scene understanding of a complex environment and results in more robust feature matching process. The resulting context-aware geospatial platform facilitates increasing complex spatiotemporal analytics for enhanced maneuverability and route planning for ground units from assistive low-altitude airborne platforms. The tools proposed show promise for increasing user security through spatial awareness in regions that are GPS-denied or unseen. In particular, these capabilities are demonstrated on a collection of sequential aerial images taken of a region with mixed use.