The Control Toolbox - An Open-Source C++ Library for Robotics, Optimal and Model Predictive Control

Filters: KF, EKF, UKF || Process Models: CV, CTRV || Measurement Models: Radar, Lidar

State estimation, smoothing and parameter estimation using Kalman and particle filters.

State estimation and filtering algorithms in Go

A compact Extended Kalman Filter (EKF) library for real time embedded system (with template for Teensy4/Arduino and STM32CubeIDE)

This is an open source Kalman filter C++ library based on Eigen3 library for matrix operations. The library has generic template based classes for most of Kalman filter variants including: (1) Kalman Filter, (2) Extended Kalman Filter, (3) Unscented Kalman Filter, and (4) Square-root UKF..

Self-position estimation by eskf by measuring gnss and imu

Kalman filter sanctuary - including continuous-discrete extended Kalman filter. Bring additional filters here for a bigger collection.

Neural-Kalman GNSS/INS Navigation for Precision Agriculture

A novel architecture in which the Factor Graph Optimization (FGO) is hybrid with the Extended Kalman Filter (EKF) for tightly coupled GNSS/UWB integration with online Temporal calibration (FE-GUT).

3D Pose Estimation of the Planar Robot Using Extended Kalman Filter

3D-PointMass-EKF for State Estimation coupled with STM-UKF for Side Slip Angle Estimation

Maximum Correntropy Kalman Filter

Modified Extended Kalman Filter with generalized exponential Moving Average and dynamic Multi-Epoch update strategy (MEKF_MAME)

Unscented Kalman Filter in C code and MATLAB code - Easy to use

This repository consists the entire solution code for the course SLAM - by Claus Brenner. All solutions have been written in Python 3. You can find the video tutorials on YouTube.

AutonomousRobots Series

A C++ implementation of the Kalman filter and its extension, the extended Kalman filter (EKF) and the unscented Kalman filter (UKF) using Eigen.

This is a relatively low level implementation of a kalman filter; with support for extended and iterative extended kalman filters. The goals of the project are to provide a numerically stable, robust EKF implementation which is both fast and portable.

Fuse camera and LIDAR sensor data to track an object using an Extended Kalman Filter and performed SLAM