Curvefitting-Least_Squares-Homography-and-RANSAC

ENPM667: Perception for Autonomous Robots

Contents

1. curve_fitting
2. least_squares
3. RANSAC
4. homography
5. ball videos (for curve fitting)
6. Kaggle data set in csv format for problem 3 (datasetp.csv)
7. ball images (extracted)
8. report

Dependencies

• python 3.9 (works for any python 3 version)
• Python running IDE. (I used PyCharm IDE to program the Code and Execute the Code)

Libraries

• import cv2
• import math
• import numpy as np
• import matplotlib.pylot as plt
• import random
• import pandas as pd

How to run the code

1. Download the zip file and extract it at whichever place comfortable
2. Install Python 3.9 and the libraries mentinoned above, prior to running the code
3. Open your IDE and open the root directory
4. If you are using any other IDE, please mention the path to the videos or datasets included in the file as indicated in the code.
5. Execute the code

Results

Curve Fitting

Least Squares

Linear Least Squares

Total Least Squares

Principle components

RANSAC

Contact Author

Name : Bharadwaj Chukkala
Email : bchukkal@terpmail.umd.edu