Project Code

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

I've done the following modifications:

• tools.cpp:
  • added RMSE calculation (from term 2, lesson 5)
• ukf.cpp:
  • added Kalman Filter update code for LIDAR and RADAR (UKF) and added new function combining code used by LIDAR and RADAR
  • tuned process noise for longitudinal and yaw acceleration using NIS

I added NIS calculation for test purpose and to tune process noise parameters for longitudinal and yaw acceleration. I ended up with values:

• 1.5 m/s^2 for longitudinal (corresponds to 5.4 km/h speed variation per sec)
• PI/4 rad/s^2 (8 sec per circle)

NIS result with these parameters is:

• 3.2% above limit for Radar (8 out of 249 measurements - limit 7.815 - 3D)
• 2.4% above limit for Lidar (6 out of 249 measurements - limit 5.991 - 2D)

With these setting the simulator result for RMSE is the following:

Unscented Kalman Filter Project Starter Code

Self-Driving Car Engineer Nanodegree Program

In this project utilize an Unscented 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. ./UnscentedKF

Note that the programs that need to be written to accomplish the project are src/ukf.cpp, src/ukf.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"]

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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

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 stick to Google's C++ style guide as much as possible.

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

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.