Hilbert Maps

Reference Python implementation of the Hilbert Map mapping method presented in the paper:

"Hilbert maps: scalable continuous occupancy mapping with stochastic gradient descent." F. Ramos and L. Ott In The International Journal of Robotics Research, 2016

Code
Hilbert Map

Stochastic Gradient Descent ICP

C++ Reference implementation of the stochastic gradient descent based ICP method presented in the paper:

"Speeding up ICP using Stochastic Gradient Descent" F. Afzal Maken, F. Ramos, L. Ott In IEEE International Conference on Robotics Automation, 2019

The code requires the following libraries

  • Eigen 3
  • boost
  • pcl

and has example programs which produce an aligned point cloud and the estimate of the transform between the two provided inputs.

Code
Hilbert Map

PHASER

Reference implementation of the PHASER algorithm described in the paper:

"PHASER: a Robust and Correspondence-free Global Pointcloud Registration." L. Bernreiter, L. Ott, J. Nieto, R. Siegwart, and C. Cadena in IEEE Robotics and Automation Letters, 2021

Code
Hilbert Map

Facility Location Outlier Detection

Basic implementation of the facility location and outlier detection algorithm presented in the paper:

"On integrated clustering and outlier detection.", L. Ott, L. Pang, F. Ramos, and S. Chawla. In Advances in Neural Information Processing Systems, 2014

Code
Hilbert Map