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 rubric.
The implementation of UKF::Prediction() and UKF::UpdateRadar() methods was based on the code found in the unscented kalman filter quizes, while methods UKF::UpdateLidar and UKF::ProcessMeasurement() are based on code found in the extended kalman filter quizes. The initial values for the process noise standard deviation longitudional and yaw acceleration where set to an initial value that was not reasonable (30). We started testing the code by setting this value to 3, as suggested in the parameters and consistency section. By trial and error, the final values, that produced the best RMSE estimates where 1.5 and 7. The final RMSE can be seen in the figure below.
The initial value for the radar NIS starts with a big value. Updating the initial P matrix fixes this issue. The NIS values for the initial and final P matrix values can be found in the data folder.
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 the uWebSocketIO Starter Guide page in the classroom within the EKF Project lesson 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.
- mkdir build
- cd build
- cmake ..
- make
- ./UnscentedKF
Tips for setting up your environment can be found in the classroom lesson for the EKF project.
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 protocol 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"]
- 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
- Clone this repo.
- Make a build directory:
mkdir build && cd build - Compile:
cmake .. && make - Run it:
./UnscentedKF
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)
Please stick to Google's C++ style guide as much as possible.
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.
This information is only accessible by people who are already enrolled in Term 2 of CarND. If you are enrolled, see the project page in the classroom for instructions and the project rubric.
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