Hilbert occupancy mapping without tuning regularization parameters. We can obtain both mean and variance estimates.
Tutorials An intuitive guide to Bayesian Hilbert maps - BHM_tutorial.ipynb
Demonstrations Now BHM is available in both numpy and pytorch (CUDA).
- first implementation - /BHM/original/demo_intel.ipynb
- pytorch CPU implementation - /BHM/pytorch/demo_kitti2d_cpu.py
- pytorch GPU implementation - /BHM/pytorch/demo_kitti2d_cuda.py - 0.5 s per scan on a laptop with a 100 m/360° LIDAR
Datasets Intel Lab dataset KITTI dataset
Videos: https://youtu.be/LDrLsvfJ0V0
Example:
import sbhm
X = #numpy array of size (N,2)
y = #numpy array of size (N,)
X_pred = #numpy array of size (N_pred,2)
model = sbhm.SBHM(gamma)
model.fit(X, y)
y_pred = model.predict(X_pred)
# with pytorch
See the demonstrations.
Papers: Introduction to Bayesian Hilbert Maps
@inproceedings{senanayake2017bayesian,
title={Bayesian hilbert maps for dynamic continuous occupancy mapping},
author={Senanayake, Ransalu and Ramos, Fabio},
booktitle={Conference on Robot Learning},
pages={458--471},
year={2017}
}
Examples with moving robots and the similarities to Gaussian process based techniques:
@inproceedings{senanayake2018continuous,
title={Building Continuous Occupancy Maps with Moving Robots},
author={Senanayake, Ransalu and Ramos, Fabio},
booktitle={Proceedings of the Thirty Second AAAI Conference on Artificial Intelligence},
year={2018},
organization={AAAI Press}
}
Learning hinge points and kernel parameters:
@inproceedings{senanayake2018automorphing,
title={Automorphing Kernels for Nonstationarity in Mapping Unstructured Environments},
author={Senanayake*, Ransalu and Tomkins*, Anthony and Ramos, Fabio},
booktitle={Conference on Robot Learning},
pages={--},
year={2018}
}
code: https://github.com/MushroomHunting/autormorphing-kernels
Fast fusion with multiple robots
@inproceedings{zhi2019fusion,
title={Continuous Occupancy Map Fusion with Fast Bayesian Hilbert Maps},
author={Zhi, William and Ott, Lionel and Senanayake, Ransalu and Ramos, Fabio},
booktitle={The International Conference on Robotics and Automation (ICRA)},
pages={--},
year={2019}
}