https://github.com/maudzung/extended-kalman-filter-cpp
Extended Kalman Filter Project using C++
https://github.com/maudzung/extended-kalman-filter-cpp
cpp kalman-estimator kalman-filter kalman-tracking
Last synced: about 1 month ago
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Extended Kalman Filter Project using C++
- Host: GitHub
- URL: https://github.com/maudzung/extended-kalman-filter-cpp
- Owner: maudzung
- Created: 2020-05-10T12:42:43.000Z (almost 5 years ago)
- Default Branch: master
- Last Pushed: 2020-05-12T07:47:26.000Z (almost 5 years ago)
- Last Synced: 2025-01-21T15:22:05.590Z (3 months ago)
- Topics: cpp, kalman-estimator, kalman-filter, kalman-tracking
- Language: C++
- Homepage:
- Size: 6.33 MB
- Stars: 3
- Watchers: 2
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Extended Kalman Filter Project using C++
In this project I utilized a kalman filter to estimate the state of a moving object of interest with noisy lidar and radar measurements.
A great note could be found [here](https://medium.com/intro-to-artificial-intelligence/extended-kalman-filter-simplified-udacitys-self-driving-car-nanodegree-46d952fce7a3)
## High level architecture of Extended Kalman Filter
## 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/)
## How to compile and run
1. Download the Term 2 Simulator [here](https://github.com/udacity/self-driving-car-sim/releases).
2. Install `uWebSocketIO`:
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/)
You can execute the `install-ubuntu.sh` to install uWebSocketIO.
3. Once the install for uWebSocketIO is complete, the main program can be built and run by doing the following from the project top directory.
```shell script
mkdir build
cd build
cmake ..
make
./ExtendedKF
```
## Results
The simulation is tracking the blue car, the initial position of the car, the RADAR and LIDAR sensors are ar the origin of the coordinates system.
- Red circles are lidar measurements.
- Blue circles are radar measurements (an arrow pointing in the direction of the observed angle).
- Green markers are the car's position as estimated by the Kalman filter.
Obviously, the Kalman filter works well on tracking the car's position with significantly reduced noise.
The Root Mean Square Error:
- X: 0.0973
- Y: 0.0855
- Vx: 0.4513
- Vy: 0.4399
The full demostrations are available at:
- [Dataset 1](https://youtu.be/HbxQKSifevc)
- [Dataset 2](https://youtu.be/W-Kf2NG4tMw)
## Generating Additional Data
See the [utilities repo](https://github.com/udacity/CarND-Mercedes-SF-Utilities) for Matlab scripts that can generate additional data.