Sharath Srinivasan (@sharathsrini)
github

sharathsrini

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Top repositories

1

Kalman-Filter-for-Sensor-Fusion

A Sensor Fusion Algorithm that can predict a State Estimate and Update if it is uncertain
Jupyter Notebook
146
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2

Particle-Filter

A Particle Filter algorithm which could be used to localize an autonomous system such as a UAV or a self-driving car.
C++
25
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3

Semantic-Segmentation-for-Kitti-Dataset

Python
6
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4

Traffic-Sign-Classifier

This is a Classifier Algorithm that can classify German Traffic-Signs. It uses the good old convolution network inspired by the Nvidia Model used in their self-driving car.
Jupyter Notebook
4
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5

Extended-Kalman-Filter-for-Sensor-Fusion

Extended Kalman Filters Are useful when there is an Non Linearity in the su=ystem and the estimation/prediction and measurement step requires a Jacobian matrix( first Derivative in the Taylor Series) is required to transform and work optimally.
Jupyter Notebook
3
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6

Advance-Lane-Detection

An Algorithm to detect the Lane by a autonomous car using image processing such as Sobel, Threshold and Hough Transform. the ROI is fit by calculating the radius of curvature by choosing three points on the lane and fittting a second order polynomial
Jupyter Notebook
2
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7

GPS_to_Local_Coordinates

GPS coordinates to Local Coordinates Conversion
Jupyter Notebook
1
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8

Vehicle-Detection

This Algorithm could help us detect and classify the vehicle on road. It implements an SVM in the backend to classify the Vehicle and non-Vehicle objects on the road. It also uses various color spaces and Hog channels to extract the features
Jupyter Notebook
1
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9

Behavioral-Clonning

This repository contains starting files for the Behavioral Cloning Project. In this project, you will use what you've learned about deep neural networks and convolutional neural networks to clone driving behavior. You will train, validate and test a model using Keras. The model will output a steering angle to an autonomous vehicle. We have provided a simulator where you can steer a car around a track for data collection. You'll use image data and steering angles to train a neural network and then use this model to drive the car autonomously around the track. We also want you to create a detailed writeup of the project. Check out the writeup template for this project and use it as a starting point for creating your own writeup. The writeup can be either a markdown file or a pdf document. To meet specifications, the project will require submitting five files: model.py (script used to create and train the model) drive.py (script to drive the car - feel free to modify this file) model.h5 (a trained Keras model) a report writeup file (either markdown or pdf) video.mp4 (a video recording of your vehicle driving autonomously around the track for at least one full lap) This README file describes how to output the video in the "Details About Files In This Directory" section.
HTML
1
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