Project Code

The project was cloned from the official CarND “Extended Kalman Filter” repository (https://github.com/udacity/CarND-Extended-Kalman-Filter-Project).

I’ve done the following modifications:

• tools.cpp:
  • added RMSE and Jacobian matrix calculation (from term 2, lessoon 5)
• kalman_filter.cpp:
  • added Kalman Filter update code for LIDAR and RADAR (EKF)
    • calculating h(x’) according to lesson 5
    • added normalization of phi
    • added new function combining code used by LIDAR and RADAR
• FusionEKF.cpp:
  • added initialization for LIDAR and RADAR (from lesson 5 and tips and tricks)
  • added prediction with updating F and Q
  • added updating by setting H and R according to sensor type (normalizing phi for RADAR)

Extended Kalman Filter Project Starter Code

Self-Driving Car Engineer Nanodegree Program

In this project utilize a kalman filter to estimate the state of a moving object of interest with noisy lidar and radar measurements. Passing the project requires 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.

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. ./ExtendedKF

Note that the programs that need to be written to accomplish the project are src/FusionEKF.cpp, src/FusionEKF.h, kalman_filter.cpp, kalman_fitler.h, tools.cpp, and tools.h

The program main.cpp has already been filled out, but feel free to modify it.

Here is the main protcol that main.cpp uses for uWebSocketIO in communicating with the simulator.

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 >= 3.5
  • All OSes: click here for installation instructions
• make >= 4.1
  • 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 path/to/input.txt path/to/output.txt. You can find
   some sample inputs in ‘data/‘.
   • eg. ./ExtendedKF ../data/obj_pose-laser-radar-synthetic-input.txt

Editor Settings

We’ve purposefully kept editor configuration files out of this repo in order to
keep it as simple and environment agnostic as possible. However, we recommend
using the following settings:

• indent using spaces
• set tab width to 2 spaces (keeps the matrices in source code aligned)

Code Style

Please (do your best to) stick to Google’s C++ style guide.

Generating Additional Data

This is optional!

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

Note: regardless of the changes you make, your project must be buildable using
cmake and make!

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

Hints!

• You don’t have to follow this directory structure, but if you do, your work
  will span all of the .cpp files here. Keep an eye out for TODOs.

Call for IDE Profiles Pull Requests

Help your fellow students!

We decided to create Makefiles with cmake to keep this project as platform
agnostic as possible. Similarly, we omitted IDE profiles in order to we ensure
that students don’t feel pressured to use one IDE or another.

However! We’d love to help people get up and running with their IDEs of choice.
If you’ve created a profile for an IDE that you think other students would
appreciate, we’d love to have you add the requisite profile files and
instructions to ide_profiles/. For example if you wanted to add a VS Code
profile, you’d add:

• /ide_profiles/vscode/.vscode
• /ide_profiles/vscode/README.md

The README should explain what the profile does, how to take advantage of it,
and how to install it.

Regardless of the IDE used, every submitted project must
still be compilable with cmake and make.