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This project implements sensor fusion algorithm using a kalman filter to estimate the state of a moving object of interest with noisy lidar and radar measurements.

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Extended Kalman Filter Project

This project utilized a kalman filter to estimate the state of a moving object of interest with noisy lidar and radar measurements. This project successfully maintained a RMSE less than or equal to [.11, .11, 0.52, 0.52] for px, py, vx, and vy respectively.

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

This project 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

The main implementation of this project can be found in the following files, src/FusionEKF.cpp, src/FusionEKF.h, kalman_filter.cpp, and kalman_filter.h

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

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

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

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

  1. ["estimate_x"] <= kalman filter estimated position x
  2. ["estimate_y"] <= kalman filter estimated position y
  3. ["rmse_x"]
  4. ["rmse_y"]
  5. ["rmse_vx"]
  6. ["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

Google's C++ style guide

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This project implements sensor fusion algorithm using a kalman filter to estimate the state of a moving object of interest with noisy lidar and radar measurements.

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