Unscented Kalman Filter Project

Self-Driving Car Engineer Nanodegree Program

The README for project starter code in order to do the code setup is at this Udacity link.

##Brief on Project. 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. It is expected to keep the RMSE (error) values, [px, py, vx, and vy] less than or equal to the values [.09, .10, .40, .30].

This project uses the Term 2 Simulator 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 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

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"]

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Other Important Dependencies

Refer Udacity link's Other Important Dependencies section.

Basic Build Instructions

After having the dependencies above met :

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

Google's C++ style guide has been used.

Outputs

Images

The outputs for estimation is in the output.txt file.