🤖 AI Summary
To address the high acquisition cost and geographic constraints of real-world aerial imagery for vision-based navigation (VBN), this paper introduces an open-source simulation framework that synthesizes spectrally faithful top-down UAV imagery from high-resolution satellite data. Methodologically, it proposes a novel zero-yaw, map-driven Python-based virtual UAV raster-scanning mechanism, enabling customizable geographic extents, flight altitudes, field-of-view angles, and overlap ratios—supporting scalable generation from single-site low-altitude to city-wide aerial datasets. The system integrates georeferenced coordinate mapping, tiled satellite data loading, a parametric camera model, and efficient sampling algorithms. Validated across the entire city of Memphis, it produces high-fidelity aerial imagery datasets with corresponding 3D world benchmarks. This framework substantially reduces VBN data collection costs and deployment barriers. Its modular, open architecture is extensible to diverse applications, including environmental monitoring and smart-city analytics.
📝 Abstract
Capturing real-world aerial images for vision-based navigation (VBN) is challenging due to limited availability and conditions that make it nearly impossible to access all desired images from any location. The complexity increases when multiple locations are involved. State-of-the-art solutions, such as deploying UAVs (unmanned aerial vehicles) for aerial imaging or relying on existing research databases, come with significant limitations. TerrAInav Sim offers a compelling alternative by simulating a UAV to capture bird's-eye view map-based images at zero yaw with real-world visible-band specifications. This open-source tool allows users to specify the bounding box (top-left and bottom-right) coordinates of any region on a map. Without the need to physically fly a drone, the virtual Python UAV performs a raster search to capture images. Users can define parameters such as the flight altitude, aspect ratio, diagonal field of view of the camera, and the overlap between consecutive images. TerrAInav Sim's capabilities range from capturing a few low-altitude images for basic applications to generating extensive datasets of entire cities for complex tasks like deep learning. This versatility makes TerrAInav a valuable tool for not only VBN but also other applications, including environmental monitoring, construction, and city management. The open-source nature of the tool also allows for the extension of the raster search to other missions. A dataset of Memphis, TN, has been provided along with this simulator. A supplementary dataset is also provided, which includes data from a 3D world generation package for comparison.