/localization

TDA based localization

Primary LanguagePython

TDA based localization

  • TODO: Include a descriptive over view paragraph of our project here

Prerequisits: TODO: Add a list of prereqs to run this program

  • Must have Anaconda installed to run the project as intended
    • Download and install Anaconda for your machine.

Installing

  • TODO: Add a step by step guide for installing the necessary tooling along side the project itself (possibly a video? does the compTag group have a channel we could upload it to for reference?)

  • Clone the project to a directory on your computer.

    • ToDo: Include example command for cloning the repo/suggesting to use the latest release from releases.

  • Naivigate to the project directory; specificially the localization folder.

  • While in the localization directory, run the following command to install the necessary conda environment:

    • $ conda env create -f environment.yaml

  • Now just use the command conda activate environment in order to set the correct python environment to run the project.

Running the Code with test.las

To get started, run main.py. You will then be prompted for the number of one dimensional partitions for your pointcloud which should be entered as an integer value. If two is chosen, eight sections will be created.

Next, when prompted for the filename, enter ‘test’ and press enter.

After this, you will be prompted with options for point cloud data to localize which can be manually selected from the sections or randomly chosen.

When prompted for the number of results desired, enter an integer value for the number of cell identifiers with the lowest bottleneck distances to the cell being searched for. The code will then evaluate the bottleneck distance between this chosen section and every other section and will return the desired number of distances.

Running tests

  • What you will need

    • TODO: Add the data/format required to run our tests on.

  • Examples

    • TODO: Provide examples of us running our tests with provided example data.

Build with

  • Anaconda
  • Dionysus
  • jsonpickle

Authors

  • George Engel
  • Clare DuVal
  • Luke Askew

Acknowledgments

  • David Millman
  • Binhai Zhu
  • Brittany Fasy