Extended Kalman Filter Project Starter Code

Self-Driving Car Engineer Nanodegree Program

In this project you will utilize 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 that 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 this concept in the classroom 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 here

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 protcol 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

Tools Create

  • RMSE:

$$ RMSE = \sqrt{\frac{1}n \sum^n_{t=1}(x^{est}_t - x^{true}_t)^2} $$

  • Jacobian matrix

$$ h(x) \approx h(\mu) +\frac{\partial h(\mu)}{\partial x}(x - \mu) $$

Sensor Fusion General Processing Flow

Kalman filter 5 key process

Call for IDE Profiles Pull Requests

I use the Visual Studio Code to edit my code , there are some useful plugins recommended to install before to use. C/C++ to make your VS code could interpret C/C++ Code Runner to make your VS code could build and run C/C++ in realtime. Markdown all in one to make your VS code could edit and preview your markdown file.

Simulator Result

  • DATA1

  • DATA2