Accumulating confidence for deep neural network object detections and semantic segmentations in sequential UAS imagery through spatiotemporal feature correspondences generated from SfM techniques
Trevor M. Bajkowski, James Alex Hurt, David Huangal, Jeffrey J. Dale, James M. Keller, Grant J. Scott, Stanton R. Price · 2021
Unmanned aerial systems (UAS) equipped with visual sensors can be quickly deployed to map novel regions, a useful ability in GPS-denied regions, search and rescue operations, disaster response, and defense. Assisted by such a UAS, a ground vehicle could safely navigate a given region, aware of the potential hazards seen from airborne sensors. Here, we propose a pipeline for identifying and mapping maneuverable regions and objects pertinent to safe navigation (cars, barriers, etc.) in sequential imagery captured from UAS sensors. First we use a semantic labeling deep neural network for identifying roads, an object detection neural network for detecting hazards of known classes, and a model that uses linear features to detect potential road hazards in labeled road pixels. This visual evidence regarding maneuverability is collected across temporally-sequential images and is spatially fused into a single map via visual feature correspondence. Fusion of road evidence is done on a per-pixel basis while clustering techniques are used to find objects given a set of co-mapped detections. We show the use of this pipeline for the quick and automated creation of maps that contain useful information with regard to safe navigation of a region captured by UAS sensors. These techniques serve as a part in the development of a model for safe, ecient navigation of GPS-denied/rapidly changing regions through the use of UAS-enabled mapping.