pavelsimo/CarND-Unscented-Kalman-Filter-Project

Self-Driving Car Nanodegree Program Unscented Kalman Filter Project

Unscented Kalman Filter Project

In this project utilize an Unscented Kalman Filter to estimate the state of a moving object of interest with noisy lidar and radar measurements.

My project includes the following files:

• ukf.cpp unscented kalman filter implementation
• tools.cpp utility functions (e.g. CalculateRMSE)
• Unscented-Kalman-Filter.ipynb notebook with NIS calculations

Passing the project requires obtaining RMSE values that are lower that the tolerance outlined in the project rubric.

Dependencies

• Udacity Term 2 Simulator
• uWebSocketIO
• 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
4. Run it: ./UnscentedKF Previous versions use i/o from text files. The current state uses i/o from the simulator.

Tips for setting up your environment can be found here

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.