General RANSAC solver using only NumPy as dependency. The implemented RANSAC solver can be used basically with any model fitting method to make it robust against outliers.

https://omyllymaki.medium.com/algorithms-from-scratch-ransac-f5a03bed2fde

Clone repo and update submodules (for samples):

git clone https://github.com/omyllymaki/ransac-solver.git --recurse-submodules

• Python >= 3.6
• Requirements

Install requirements by running (also sample dependencies are installed):

pip3 install -r samples/requirements.txt

To use RANSAC solver, user needs to implement abstract Data and Model classes:

class Data(BaseData):
    ...

class Model(BaseModel):
    ...

After that, solver can be initialized with the model and used with the data:

solver = RansacSolver(model=Model(...), ...)
inlier_indices = solver.fit(Data(...))

See samples folder for more detailed usage. Run all the samples by typing

./run_samples.sh

Curve fit with and without RANSAC:

ICP fit with and without RANSAC:

Polynomial fit with RANSAC: