Implementation of a simple 2D EKF with lidar and radar measurements
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Updated
Feb 5, 2018 - C++
Implementation of a simple 2D EKF with lidar and radar measurements
State Estimation of a 3D Quad with the use of Bayes Rules (Extended Kalman Filter)
Udacity Flying Car Nanodegree Project #4: Quadrotor 3D Estimation
EKF Monocular SLAM written in C++20
Statistical tests for use with the Extended Kalman Filter
GUI App with implementation of class Kalman Filter for real data recorded by MPU6050. The data read from the IMU are generated in graphs together with the data estimated by the Kalman Filter.
Extended Kalman Filter
Scientific tool for estimation problems.
Use of EKF to track a moving object
This is the final project in Udacity's Flying Car and Autonomous Flight Engineer Nanodegree which covers the estimation portion of a flight controller.
Extended Kalman filter in C++ used to track the position of an object close to a car using combined radar and laser data.
C++ project for the FCND estimation project.
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).)
Estimate your electricity usage & cost for various timeframes. User-friendly, detailed breakdowns, well-structured code.
Guided Random Testing for Floating-Point Error Estimation
C++ project for the FCND estimation.
Frame by frame vehicle detection and estimation of density of traffic in real time -C++
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