Localization and Change Detection Through Aerial Environment Perception

Urban development is advancing at an exceedingly rapid pace, significantly complicating the task of updating reference maps and requiring considerable effort in terms of actualization and monitoring. To address this challenge, we propose an automated mechanism for detecting changes in maps, specifically at the building level. Our method integrates cadastral maps with low-altitude aerial imagery, identifying discrepancies between the reference maps and the perceived images. We use captured images from standard Unmanned Aerial Vehicles and derive the cadastral maps from OpenStreetMap. The change detection process employs a mechanism that localizes the drone's position by aligning the perceived scene with the reference map. We utilize established foundation models, such as Grounded SAM and Depth Anything, for image-level perception based on semantic segmentation and depth estimation, capitalizing on their robustness and generalization capabilities. Subsequently, we construct a bird's-eye view representation that mirrors the reference map and generate a set of discriminative features. The obtained features are utilized to formulate hypotheses as rigid transformations, which are tested by projecting the image onto the reference map and validated using the Intersection over Union (IoU) metric. Once the perceived image is aligned with the reference map, we obtain the global GPS position and camera orientation, implicitly determining the global location of the buildings. After achieving accurate localization, the focus shifts to identifying differences between the perceived image and the reference map. In terms of experimental results, we tested the method in an aerial environment using a subset of the UAVid dataset, considering significant GPS disturbances, and achieving high precision in localization regarding both the position and the camera orientation.