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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 "GBEES" repsository containes the codebase accompying the paper "State Estimation of Chaotic Trajectories: A Higher-Dimensional, Grid-Based, Bayesian Approach to Uncertainty Propagation" presented at the January 2024 AIAA/AAS Space Flight Mechanics Meeting.
Utilize a kalman filter to estimate the state: position_x, position_y, velocity_x,velocity_y of a moving object of interest with noisy lidar and radar measurements.
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.
In this project, I developed the estimation portion of my controller for the drone in the Udacity CPP simulator. My simulated quad is now able to fly with my estimator and my custom controller (from project 3).)
SBG ROS2 Driver: A ROS2-compatible repository providing seamless integration with SBG Systems' Inertial Measurement Unit (IMU) for precise state estimation in autonomous vehicles.