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Hilbert Maps

This repository contains the code which implements the method of the paper "Hilbert maps: scalable continuous occupancy mapping with stochastic gradient descent" by Fabio Ramos and Lionel Ott presented in RSS 2105.

@INPROCEEDINGS{Ramos-RSS-15, 
    AUTHOR    = {Fabio Ramos AND Lionel Ott}, 
    TITLE     = {Hilbert maps: scalable continuous occupancy mapping with stochastic gradient descent}, 
    BOOKTITLE = {Proceedings of Robotics: Science and Systems}, 
    YEAR      = {2015}
}

Software Requirements

To run the code you need the following software components:

Running the Example

The script example.py is a simple way to produce a map using hilbert maps from carmen style logfiles as follows:

example.py
    [-h]
    [--components COMPONENTS]
    [--gamma GAMMA]
    [--distance_cutoff DISTANCE_CUTOFF]
    [--resolution RESOLUTION]
    logfile
    {sparse,fourier,nystroem}

Only the logfile parameter and the feature type (sparse, fourier, or nystroem) is required. For more detailed parameter description use example.py --help.

For the intel dataset the following component numbers are decent starting choices.

  • sparse 1000
  • fourier 10000
  • nystroem 1000