Udacity: Self-Driving Car Engineer Nanodegree | Project: Extended Kalman Filter
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Updated
Oct 9, 2017 - C++
Udacity: Self-Driving Car Engineer Nanodegree | Project: Extended Kalman Filter
Extended kalman filter to estimate vehicle position from noisy lidar and radar data.
Self-driving Car Nano-degree. Term 2: Sensor Fusion. Project 1: Extended Kalman Filter
Submission for EKF Project
Utilized a Kalman Filter to estimate the state of a moving object of interest with noise
Implementation of a simple 2D EKF with lidar and radar measurements
A target tracking toolbox developed in Python. It aims to demonstrate how target tracking works and to serve as a testing environment for target tracking problems.
Using a Kalman Filter to estimate the state of a moving object of interest with noisy Lidar and Radar measurements
Implementation of a simple 2D UKF and EKF using an CTRV kinematics model with lidar and radar measurements.
Extended Kalman Filter Project for Self-Driving Car ND
2D object tracking with Extended Kalman filter
Utilize an Extended Kalman Filter to estimate the state of a moving object of interest with noisy Lidar and Radar measurements
🏎️ Extended Kalman Filter (EKF) Localization Project using C++ and Eigen library for the Self-Driving Car Nanodegree at Udacity
Extended Kalman Filter predicting the position of a Bug.
C++ project for the FCND estimation.
Animal tracking using machine learning with limited computational power
Self-Driving Car Nanodegree Program Starter Code for the Extended Kalman Filter Project
TinyEKF for micropython/python
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