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Sun-BCNN

Bayesian Convolutional Neural Network to infer Sun Direction from a single RGB image, trained on the KITTI dataset [1].

Installation & Pre-Requisites

  1. Download and compile Caffe-Sl (we use their L2Norm layer).

  2. Ensure that the lmdb and cv2 python packages are installed (e.g. through pip).

  3. Clone sun-bcnn:

git clone https://github.com/utiasSTARS/sun-bcnn-vo.git

Testing with pre-trained model

  1. Visit ftp://128.100.201.179/2016-sun_bcnn to download the pre-trained models from the models folder along with a test LMDB file.

  2. Edit caffe-files/test_sunbcnn.sh to match appropriate mean file, weights file and testing file. Edit scripts/test_sunbcnn.py with appropriate directories.

  3. Run scripts/test_sunbcnn.sh:

bash scripts/test_sunbcnn.sh

Training

Using KITTI data

Coming soon...

Using your own data

You're on your own!

Citation

V. Peretroukhin, L. Clement, J. Kelly. Reducing Drift in Visual Odometry by Inferring Sun Direction using a Bayesian Convolutional Neural Network

Submitted to ICRA 2016. Pre-print available: arXiv:1609.05993.

SUN-BCNN

References

[1] A. Geiger, P. Lenz, C. Stiller, and R. Urtasun, "Vision meets robotics: The KITTI dataset," Int. J. Robot. Research (IJRR), vol. 32, no. 11, pp. 1231–1237, Sep. 2013. http://www.cvlibs.net/datasets/kitti/

[2] Y. Gal and Z. Ghahramani, “Dropout as a bayesian approxi- mation: Representing model uncertainty in deep learning,” in Proceedings of The 33rd International Conference on Machine Learning, 2016, pp. 1050–1059.