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Extended Kalman Filter for sensor fusion Radar and LiDAR inputs to track an object

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Udacity - Self-Driving Car NanoDegree Udacity - Self-Driving Car P3

Extended Kalman Filter Project

Self-Driving Car Engineer Nanodegree Program

In this project I utillized a kalman filter to estimate the state of a moving object of interest with noisy lidar and radar measurements. My implimantation is obtaining RMSE values that are lower than the tolerance required.

This project involves the a Simulator (Made by Udacity Team) 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

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

Code Style

I followed Google's C++ style guide.

Generating Additional Data

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

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.

Hints and Tips!

  • Students have reported rapid expansion of log files when using the term 2 simulator. This appears to be associated with not being connected to uWebSockets. If this does occur, please make sure you are conneted to uWebSockets. The following workaround may also be effective at preventing large log files.

    • create an empty log file
    • remove write permissions so that the simulator can't write to log
  • Please note that the Eigen library does not initialize VectorXd or MatrixXd objects with zeros upon creation.

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Extended Kalman Filter for sensor fusion Radar and LiDAR inputs to track an object

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