Unscented Kalman Filter Project Starter Code

Self-Driving Car Engineer Nanodegree Program

In this project Unscented Kalman Filter is utilized to estimate the state of a moving object of interest with noisy lidar and radar measurements. Passing the project required obtaining RMSE values that are lower that the tolerance outlined in the project reburic.

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 intall 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 ran by doing the following from the project top directory.

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

Tips for setting up your environment can be found here

Protocol of main.cpp

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"]

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Other Important Dependencies

• 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.

Unscented vs Extended Kalman Filter

Kalman filters have the same mains steps: 1. Initialization, 2. Prediction, 3. Update. The Prediction and Update processes can be linear and non-linear. A Standard Kalman Filter (KF) can be used when the process is linear but when it is not there are two options: Extended and Unscented Kalman Filters. Extended Kalman Filter uses the Jacobian matrix to linearize non-linear functions; Unscented Kalman Filter, on the other hand, does not need to linearize non-linear functions, instead, the Unscented Kalman Filter maps representative points from a Gaussian distribution.

Results

Input	RMSE
X	0.0671
Y	0.0816
Vx	0.2939
Vy	0.2669

Code Style

A modified version of the Google's C++ style guide has been used. Clang format was used to make sure of the style format.

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.

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.

Discussion

• implementation of UpdateLidar function can be broken down into further functions
• code optimizations still possible
• experimentation to figure out optimal parameters