https://github.com/simondlevy/TinyEKF

Lightweight C/C++ Extended Kalman Filter with Python for prototyping
https://github.com/simondlevy/TinyEKF

Last synced: 3 months ago
JSON representation

Lightweight C/C++ Extended Kalman Filter with Python for prototyping

• Host: GitHub
• URL: https://github.com/simondlevy/TinyEKF
• Owner: simondlevy
• License: other
• Created: 2015-10-16T00:08:38.000Z (about 9 years ago)
• Default Branch: master
• Last Pushed: 2024-05-17T22:14:00.000Z (6 months ago)
• Last Synced: 2024-07-01T02:02:53.648Z (4 months ago)
• Language: Python
• Homepage:
• Size: 2.38 MB
• Stars: 971
• Watchers: 58
• Forks: 328
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE.md

Awesome Lists containing this project

• Awesome-arduino - TinyEKF - Lightweight C/C++ Extended Kalman Filter with Arduino example (Libraries)
• awesome-arduino - TinyEKF - Lightweight C/C++ Extended Kalman Filter with Arduino example (Libraries)

README

        
# TinyEKF: Lightweight C/C++ Extended Kalman Filter with Python for prototyping





TinyEKF is a simple, header-only C/C++ implementation of the

[Extended Kalman Filter](https://simondlevy.github.io/ekf-tutorial/) 

that is general enough to use on different projects.  It supports both single- and

double-precision floating-point computation.  In order to make it practical for

running on Arduino, STM32, and other microcontrollers, it uses static

(compile-time) memory allocation (no "new" or "malloc").  The **examples**

folder includes both a "pure C" example from the literature, as well as an

Arduino example of sensor fusion.  The **python** folder includes a Python

class that you can use to prototype your EKF before implementing it in C or C++.

Arduino users can simply install or drag the whole TinyEKF folder into their Arduino libraries folder. 

The **examples/SensorFusion** folder contains a little sensor fusion example using a 

[BMP180 barometer](https://www.sparkfun.com/products/11824) and 

[LM35 temperature sensor](http://www.robotshop.com/en/dfrobot-lm35-linear-temperature-sensor.html).

I have run this example on an Arduino Uno and a Teensy 3.2. The BMP180, being an I^2C sensor, should be connected

to pins 4 (SDA) and 5 (SCL) of the Uno, or pins 18 (SDA) and 19 (SCL) of the Teensy.  For other Arduino boards,

consult the [documentation](https://www.arduino.cc/en/Reference/Wire) on the Wire library. The analog output

from the LM35 should go to the A0 pin of your Arduino or Teensy.

In addition to the class definition, the **python** folder has an example of mouse tracking, using OpenCV. 

So you will have to install OpenCV to run this example. There is also a sensor-fusion example in this folder.