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A Kalman Kilter is used to estimate the state of a moving object of interest with noisy lidar and radar measurements. RMSE values are lower than a specified tolerance.

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davidscmx/extended-kalman-filter

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Extended Kalman Filter Project

In this project a Kalman Kilter is used to estimate the state of a moving object of interest with noisy lidar and radar measurements. RMSE values are lower than a specified tolerance.

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

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

Installing and Using Eigen in Your Project

System-Wide Installation on Ubuntu 22.04

  1. Update package list:

    sudo apt update
    
  2. Install Eigen3:

    sudo apt install libeigen3-dev
    
  3. Verify installation:

    pkg-config --modversion eigen3
    

Eigen headers are installed in /usr/include/eigen3/.

Using Eigen in Your Project

  1. Include Eigen headers in your C++ files:

    #include <Eigen/Dense>
  2. When compiling, add this flag:

    -I/usr/include/eigen3
    
  3. If using CMake, add to your CMakeLists.txt:

    find_package(Eigen3 3.3 REQUIRED NO_MODULE)
    target_link_libraries(your_target_name Eigen3::Eigen)

After following these steps, Eigen will be available system-wide, eliminating the need to include it in your source directory.

Updating Your Current CMakeLists.txt

To integrate Eigen into your existing project, update your CMakeLists.txt:

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A Kalman Kilter is used to estimate the state of a moving object of interest with noisy lidar and radar measurements. RMSE values are lower than a specified tolerance.

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