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PARPedraza / GaussianDensityModel

We proposes a novel and robust 3D object segmentation method, the Gaussian Density Model (GDM) algorithm. The algorithm works with point clouds scanned in the urban environment using the density metrics, based on existing quantity of features in the neighborhood. The LiDAR Velodyne 64E was used to scan urban environment.
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computer-vision density-based-clustering density-metrics gdm machine-learning neighborhood python segmentation urban-environment

readme

==================== Gaussian Density Model

Segmentation algorithm of urban environment, we used the LiDAR Velodyne 64E. The algorithm read and write type files csv.

Input: cloud points (x,y,z,d) where d is density and (x,y,z) without density.

Ouput: cloud points (x,y,z,d) objects segmentation.

==================

Required Modules:

Installation:

$ sudo apt update

$ sudo apt upgrade

$ pip install numpy

$ pip install math

$ pip install pandas

$ pip install python-csv

$ pip install matplotlib

$ pip install itertools-s

Example:

$ python GDM.py --help

$ python GDM.py -i iValue

Cite article (In review):

@article{article, author = {A. R. Pedraza, J. J. G. Barbosa, K. L. F. Rodríguez, A. I. G. Moreno and E. A. G. Barbosa}, year = {2019}, month = {}, pages = {}, title = {Free-form object segmentation in urbanenvironments using Gaussian Density Model}, volume = {}, journal = {Latin America Transactions, IEEE (Revista IEEE America Latina}, doi = {10.1109/TLA.} }

.. image:: https://img.shields.io/badge/license-AGPL--3-blue.png :target: https://www.gnu.org/licenses/agpl :alt: License: AGPL-3