/Point_Cloud_Analysis

Estimating Ground Surface Normals and Fitting Surfaces to Noisy LIDAR Point Cloud Data

Primary LanguagePython

Point Cloud Analysis

image1 image2 image3 image4

File Tree

PCA
+-figures
  +- 10+ images
+-pc1.csv
+-pc2.csv
+-Problem_2
+-README.md
+-report.pdf

Installation and Running

  1. Download and extract the files.

  2. Run the code Problem_2.py using the following command in your terminal python3 Problem_2.py It contains the code for both the subquestions. SEVEN pop up windows: window 1: Visualization of the surface normal to pc1. window 2: Standard Least Square fitting for pc1. window 3: Standard Least Square fitting for pc2. window 4: Total Least Square fitting for pc1. window 5: Total Least Square fitting for pc2. window 6: RANSAC for pc1. window 7: RANSAC for pc2. The terminal displays the following:

  • Covariance Matrix.
  • Eigenvalues of covariance matrix.
  • Direction and Magnitude of the surface normal.
  • Equation of the Plane for RANSAC on pc1 and its inliers.
  • Equation of the Plane for RANSAC on pc2 and its inliers.

(All the figures in pop up windows are also saved in the figures folder)

  1. A detailed report of the entire project is given in report.pdf