Extended Kalman Filters Project

Shameless plug: While you're here, you may want to have a look at my libfixkalman library for fixed-point Kalman filters in embedded systems.

This project utilizes both a regular and extended 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.

WebSocket communication

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 files that need to be completed to accomplish the project are src/FusionEKF.cpp, src/FusionEKF.h, kalman_filter.cpp, kalman_filter.h, tools.cpp, and tools.h. The 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"]

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Other Important Dependencies

• cmake >= 3.5
  • All OSes: click here for installation instructions
• make >= 4.1 (Linux, Mac), 3.81 (Windows)
  • Linux: make is installed by default on most Linux distros
  • Mac: install Xcode command line tools to get make
  • Windows: Click here for installation instructions
• gcc/g++ >= 5.4
  • Linux: gcc / g++ is installed by default on most Linux distros
  • Mac: same deal as make - install Xcode command line tools
  • Windows: recommend using MinGW

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

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.