This project contains code for stitching large number of images using classical computer vision techniques for application in drone mapping.

Install conda and prepare a python environment using the following steps:

Build a new conda environment

conda create -n image_stitching python=3.8

Install additional libraries using pip:

python3 -m pip install -r requirements.txt

Activate the conda environment:

conda activate image_stitching

• There is atleast 70% overlap (both front and sideways) between the images while the data was collected
• The images are numbered sequentially in the dataset

Below is a high-level overview of the image stitching algorithm:

1. Feature detection using SIFT
2. Feature matching using FLANN-basd matcher
3. Homography estimation using DLT and RANSAC
4. Image warping

The algorithm is tested on two datasets: agricultural farm and an urban area. Below images shows two examples mosaics:

Two possible improvements over our work is to use bundle adjustment to reduce the cumulative error issue and blending to remove noticeable seams at the stitching location.