/UKF-Project

My Udacity Unscented Kalman Filter project

Primary LanguageC++

Unscented Kalman Filter Project

This is my implementation of the UKF project for Udacity's Self-Driving Car Engineer Nanodegree Program.

Project introduction

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

This project involves 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.

Once the install for uWebSocketIO is complete, the main program can be built and ran by doing the following from the project top directory.

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

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

["sensor_measurement"] => the measurment 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

  • cmake >= v3.5
  • make >= v4.1
  • gcc/g++ >= v5.4

Basic Build Instructions

  1. Clone this repo.
  2. Make a build directory: mkdir build && cd build
  3. Compile: cmake .. && make
  4. Run it: ./UnscentedKF path/to/input.txt path/to/output.txt. You can find some sample inputs in 'data/'.
    • eg. ./UnscentedKF ../data/obj_pose-laser-radar-synthetic-input.txt

Project Instructions and Rubric

This information is only accessible by people who are already enrolled in Term 2 of CarND. If you are enrolled, see the project page for instructions and the project rubric.