Key Region Navigation

Visual navigation has recently seen significant developments with the rise in autonomous navigation. Keypoint-based mapping
and localization has served as a reliable localization method for many applications, but the push to run more applications on
less expensive hardware becomes extremely limiting. In this paper, we present a novel approach for visual geolocalization and
navigation that improves landmark detection reliability while reducing reference map complexity. Similar to prior techniques,
we use the process of point based matching schemes to solve for the image-to-map transform. The critical difference is that we
use object detection to identify key-regions instead of keypoints. During an initial flight key-regions are mapped into an identity
dictionary with their geolocations and few-shot learning encoded descriptors. Then on subsequent flights, key-regions are detected
and matched using the identity dictionary for re-identification. Using the identified vehicles as key-regions, the results show that
the proposed key-region based localization produces GPS like localization while maintaining a higher resilience to image noise
compared to keypoint-based techniques.