Image Mosaic Generation

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Large Scale Image Mosaic Construction for Agricultural Applications


We present a novel technique for stitching images obtained from aerial vehicles flying at low altitudes. Existing image stitching/mosaicking methods rely on inter-image homography computation based on a planar scene assumption. This assumption holds when images are taken from high-altitudes (hence the depth variation is negligable). It can fail when flying at low altitudes. Further, to avoid scale and resolution changes, existing methods rely on primarily translational motion at fixed altitudes.

Our method removes these limitations and performs well even when aerial images are taken from low altitudes by an aerial vehicle performing complex motions. It starts by extracting the ground geometry from a sparse reconstruction of the scene obtained from a small fraction of the input images. Next, it selects the best image (from the entire sequence) for each location on the ground using a novel camera selection criterion. This image is then independently rectified to obtain the corresponding portion of the panorama. Therefore, the technique avoids performing a costly joint-optimization over the entire sequence. It is validated using challenging input sequences motivated by agricultural applications.

  • See the Technical Report for comparisons with existing methods and technical details
  • IEEE Robotics and Automation Letters to appear

Data Sets

Available here

The directory contains images, calibration matrices and results used in the paper.


UMN MnDrive