/carnd-term2-project1-extended-kalman-filters

Primary LanguageC++

*** Archieved: As this repo is and will not updated anymore, it is archieved. ***

Extended Kalman Filter Project Code

Self-Driving Car Engineer Nanodegree Program

System: DELL XPS 13 9350, Windows 10 Bash on Ubuntu (Linux Subsystem)

The goals / steps of this project are the following:

Utilize a kalman filter to find the state of a moving object according to radar and lidar measurements. Calculate RMSE according to ground-truth data. Project steps:

  • Code the Predict and Update functions to successfully build a Kalman filter.
  • Code the RMSE and Jacobian functions.
  • Code the KalmanFilter and ExtendedKalmanFilter functions to calculate predict and update states.
  • Initialize the state vectors and covariance matrices using the first measurements.
  • Predict the object position to current timestep and after recieving measurement update the prediction.
  • Call correct measurment function (linear (laser), non-linear (radar)) according to sensor type.

Project Setup:

This project involves the Term 2 Simulator which can be downloaded here

Step 1: Install Windows 10 Bash on Ubuntu. Follow the link for a nice guide

Step 2: Install mobaxterm so you can use Sublime (or any other text editor) inside the Bash.

Step 3: Instead of Step 1 and 2 you can follow this thread which also explains how to install Sublime, a nice text editor to be used on Ubuntu.

Data Set 1 Full Run

Data Set 1: Full Run

Data Set 1 Detail

Data Set 1: Detail

Data Set 1 Fail

What Is Going On?

Why is this happening?

Altough we have proper radar and lidar data at the curvature we are exceeding 2xPi at Y direction. So we should be limiting the maximum angle between -Pi and Pi with:

if (Y(1) *180/pi > 360){
		Y(1) = 0.001;
	}

Data Set 2 Full Run

Data Set 2: Full Run