fusion-ekf-python - An extended Kalman Filter implementation in Python for fusing lidar and radar sensor measurements

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This is an extended Kalman Filter implementation in C++ for fusing lidar and radar sensor measurements. A Kalman filter can be used anywhere you have uncertain information about some dynamic system, and you want to make an educated guess about what the system is going to do next. This extended kalman filter does just that.




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MSF - Modular framework for multi sensor fusion based on an Extended Kalman Filter (EKF)

open_vins - An open source platform for visual-inertial navigation research.

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Welcome to the OpenVINS project! The OpenVINS project houses some core computer vision code along with a state-of-the art filter-based visual-inertial estimator. The core filter is an Extended Kalman filter which fuses inertial information with sparse visual feature tracks. These visual feature tracks are fused leveraging the Multi-State Constraint Kalman Filter (MSCKF) sliding window formulation which allows for 3D features to update the state estimate without directly estimating the feature states in the filter. Inspired by graph-based optimization systems, the included filter has modularity allowing for convenient covariance management with a proper type-based state system. Please take a look at the feature list below for full details on what the system supports. ov_secondary - This is an example secondary thread which provides loop closure in a loosely coupled manner for OpenVINS. This is a modification of the code originally developed by the HKUST aerial robotics group and can be found in their VINS-Fusion repository. Here we stress that this is a loosely coupled method, thus no information is returned to the estimator to improve the underlying OpenVINS odometry. This codebase has been modified in a few key areas including: exposing more loop closure parameters, subscribing to camera intrinsics, simplifying configuration such that only topics need to be supplied, and some tweaks to the loop closure detection to improve frequency.

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

  •    C

TinyEKF is a simple C/C++ implementation of the Extended Kalman Filter that is general enough to use on different projects. 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 an Arduino example of sensor fusion. The extras/python folder includes an abstract Python class that you can use to prototype your EKF before implementing it in C/C++. The extrasc/c folder contains a "pure C" example from the literature. 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 and LM35 temperature sensor. 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 on the Wire library. The analog output from the LM35 should go to the A0 pin of your Arduino or Teensy.

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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 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 this concept in the classroom for the required version and installation scripts.

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Kalman Filter

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A C++, object-oriented, Extended Kalman Filter library.

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The code is released under the GNU General Public License. This is a Kalman filter library for any microcontroller that supports float math.

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