https://github.com/yunjaechoi/sdcnd-unscentedkalmanfilter
Udacity Self Driving Car ND Unscented Kalman Filter project
https://github.com/yunjaechoi/sdcnd-unscentedkalmanfilter
udacity-self-driving-car unscented-kalman-filter
Last synced: 7 months ago
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Udacity Self Driving Car ND Unscented Kalman Filter project
- Host: GitHub
- URL: https://github.com/yunjaechoi/sdcnd-unscentedkalmanfilter
- Owner: YunjaeChoi
- License: mit
- Created: 2018-07-02T01:55:46.000Z (over 7 years ago)
- Default Branch: master
- Last Pushed: 2018-07-02T04:22:18.000Z (over 7 years ago)
- Last Synced: 2025-01-19T18:48:08.897Z (9 months ago)
- Topics: udacity-self-driving-car, unscented-kalman-filter
- Language: C++
- Size: 845 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# Unscented Kalman Filter Project
Udacity 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.
This project involves the Term 2 Simulator which can be downloaded [here](https://github.com/udacity/self-driving-car-sim/releases)
This repository includes two files that can be used to set up and intall [uWebSocketIO](https://github.com/uWebSockets/uWebSockets) for either Linux or Mac systems. For windows you can use either Docker, VMware, or even [Windows 10 Bash on Ubuntu](https://www.howtogeek.com/249966/how-to-install-and-use-the-linux-bash-shell-on-windows-10/) to install uWebSocketIO. Please see [this concept in the classroom](https://classroom.udacity.com/nanodegrees/nd013/parts/40f38239-66b6-46ec-ae68-03afd8a601c8/modules/0949fca6-b379-42af-a919-ee50aa304e6a/lessons/f758c44c-5e40-4e01-93b5-1a82aa4e044f/concepts/16cf4a78-4fc7-49e1-8621-3450ca938b77) 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](https://classroom.udacity.com/nanodegrees/nd013/parts/40f38239-66b6-46ec-ae68-03afd8a601c8/modules/0949fca6-b379-42af-a919-ee50aa304e6a/lessons/f758c44c-5e40-4e01-93b5-1a82aa4e044f/concepts/23d376c7-0195-4276-bdf0-e02f1f3c665d)
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"]
---
## Other Important Dependencies
* cmake >= 3.5
* All OSes: [click here for installation instructions](https://cmake.org/install/)
* 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](https://developer.apple.com/xcode/features/)
* Windows: [Click here for installation instructions](http://gnuwin32.sourceforge.net/packages/make.htm)
* 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](https://developer.apple.com/xcode/features/)
* Windows: recommend using [MinGW](http://www.mingw.org/)
## 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.
## 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](https://google.github.io/styleguide/cppguide.html) 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](https://github.com/udacity/CarND-Mercedes-SF-Utilities) 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](https://classroom.udacity.com/nanodegrees/nd013/parts/40f38239-66b6-46ec-ae68-03afd8a601c8/modules/0949fca6-b379-42af-a919-ee50aa304e6a/lessons/c3eb3583-17b2-4d83-abf7-d852ae1b9fff/concepts/f437b8b0-f2d8-43b0-9662-72ac4e4029c1)
for instructions and the project rubric.
## How to write a README
A well written README file can enhance your project and portfolio. Develop your abilities to create professional README files by completing [this free course](https://www.udacity.com/course/writing-readmes--ud777).