/Extended-Kalman-Filter-Project

Extended Kalman Filter Project for Udacity Self-Driving Car Nanodegree Program

Primary LanguageC++MIT LicenseMIT

Result

Extended Kalman Filter Project

Self-Driving Car Engineer Nanodegree Program

In this project we utilized a kalman filter to estimate the state of a moving object of interest with noisy lidar and radar measurements. Passing the project requires obtaining RMSE values that are lower than the tolerance outlined in the project rubric.

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

This repository includes two files that can be used to set up and install uWebSocketIO for either Linux or Mac systems. For windows you can use either Docker, VMware, or even Windows 10 Bash on Ubuntu to install uWebSocketIO. Please see the uWebSocketIO Starter Guide page in the classroom within the EKF Project lesson for the required version and installation scripts.

Once the install for uWebSocketIO is complete, the main program can be built and run by doing the following from the project top directory.

  1. mkdir build
  2. cd build
  3. cmake ..
  4. make
  5. ./ExtendedKF

Tips for setting up your environment can be found in the classroom lesson for this project.

Note that the programs that need to be written to accomplish the project are src/FusionEKF.cpp, src/FusionEKF.h, kalman_filter.cpp, kalman_filter.h, tools.cpp, and tools.h

The program main.cpp has already been filled out, but feel free to modify it.

Here is the main protocol that main.cpp uses for uWebSocketIO in communicating with the simulator.

INPUT: values provided by the simulator to the c++ program

["sensor_measurement"] => the measurement that the simulator observed (either lidar or radar)

OUTPUT: values provided by the c++ program to the simulator

["estimate_x"] <= kalman filter estimated position x ["estimate_y"] <= kalman filter estimated position y ["rmse_x"] ["rmse_y"] ["rmse_vx"] ["rmse_vy"]

Other Important Dependencies

Basic Build Instructions

  1. Clone this repo.
  2. Make a build directory: mkdir build && cd build
  3. Compile: cmake .. && make
    • On windows, you may need to run: cmake .. -G "Unix Makefiles" && make
  4. Run it: ./ExtendedKF

Editor Settings

We've purposefully kept editor configuration files out of this repo in order to keep it as simple and environment agnostic as possible. However, we recommend using the following settings:

  • indent using spaces
  • set tab width to 2 spaces (keeps the matrices in source code aligned)

Code Style

Please (do your best to) stick to Google's C++ style guide.

Generating Additional Data

This is optional!

If you'd like to generate your own radar and lidar data, see the utilities repo for Matlab scripts that can generate additional data.

Project Instructions and Rubric

Note: regardless of the changes you make, your project must be buildable using cmake and make!

More information is only accessible by people who are already enrolled in Term 2 of CarND. If you are enrolled, see the project resources page for instructions and the project rubric.

Flow Charts

1- Code Flow

This is the flow of the code. You can see how the .cpp files related each others.

  • scr a directory with the project code:
    • main.cpp - reads in data, calls fusionEKF to run the Kalman filter and calls tools to calculate RMSE.
    • FusionEKF.cpp - initializes the filter, calls the predict function and calls the update function according to sensor type.
    • kalman_filter.cpp- defines the predict function, the update function for lidar, and the updateEKF function for radar.
    • tools.cpp - a function to calculate RMSE and the Jacobian matrix
  • data a directory with two input files, provided by Udacity
  • results a directory with output and log files
  • Docs a directory with files formats description
  • extra a directory with detailed information used hardware and software (extra/additional_info.txt file) and screenshots of the final RMSE.

Source : Directory structure

2- Process flow