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Explore the world of UAV-State-Estimation, a detailed Python repository focusing on 3D state estimation for unmanned aerial vehicles (UAVs) through the use of Kalman Filter methods. This repository uniquely merges theoretical frameworks and hands-on simulations, making it an ideal resource for both drone enthusiasts and experts in drone technology.
The programs written over the summer of 2021 while working for the University of Delaware's Information and Decision Sciences (IDS) Lab. For more information about the lab and its other projects, please visit https://sites.udel.edu/ids-lab/ . This repository and README will be updated somewhat reguarly as progress is made on these projects.
The `KalmanFilter` class implements the Kalman Filter algorithm for estimating the state of linear dynamic systems using noisy measurements. The class accepts system matrices, initial state, and covariance, and provides `predict` and `update` methods for state prediction and refinement based on new observations.
SBG ROS2 Driver: A ROS2-compatible repository providing seamless integration with SBG Systems' Inertial Measurement Unit (IMU) for precise state estimation in autonomous vehicles.
This is a simple simulator for a conveyor belt, with the purpose to enable research and test algorithms related to state estimation and tracking of objects on the conveyor.
Implementation of several popular Kalman filter nonlinear variants intended for robotics systems and vehicle state estimation, including Extended Kalman Filter, Unscented Kalman Filter, Error State EKF, Invariant EKF, Square Root EKF, Cubature KF.