Extended Kalman Filter Project

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In this project we will utilize a kalman filter to estimate the state of a moving object of interest with noisy lidar and radar measurements. Extended Kalman filter is built using C++, detecting objects and estimate the state of a moving object.

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Input and Output of the program

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"]

Output from the Simulator

Here are the output of the final model.

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Simulator

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

Important Dependencies

• uWebSocketIO for either Linux or Mac systems.
• Windows 10 Bash on Ubuntu for Windows system.
• 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