https://github.com/nazanin1369/extended-kalman-filter
Extended Kalman Filter for Self Driving cars in c++
https://github.com/nazanin1369/extended-kalman-filter
Last synced: 6 days ago
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Extended Kalman Filter for Self Driving cars in c++
- Host: GitHub
- URL: https://github.com/nazanin1369/extended-kalman-filter
- Owner: Nazanin1369
- Created: 2017-08-05T21:48:38.000Z (about 8 years ago)
- Default Branch: master
- Last Pushed: 2017-08-13T04:59:19.000Z (about 8 years ago)
- Last Synced: 2025-07-07T02:50:03.242Z (3 months ago)
- Language: C++
- Size: 3.66 MB
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Extended Kalman Filter Project
In this project I utilized 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 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 install [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 run by doing the following from the project top directory.
1. mkdir build
2. cd build
3. cmake ..
4. make
5. ./ExtendedKF
INPUT: values provided by the simulator to the c++ program
["sensor_measurement"] => the measurement 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: 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`
* On windows, you may need to run: `cmake .. -G "Unix Makefiles" && make`
4. Run it: `./ExtendedKF `
## Code Style
Code is following the [Google's C++ style guide](https://google.github.io/styleguide/cppguide.html).
## Live Running Here http://recordit.co/IG852O7Rd8