Utilized an Extended Kalman Filter and Sensor Fusion to estimate the state of a moving object of interest with noisy lidar and radar measurements. The project involved utilzing lidar data (Point Cloud) for position and radar data (Doppler) for radial velocity.

Utilize an Extended Kalman Filter to estimate the state of a moving object of interest with noisy Lidar and Radar measurements

Implementation of the Extended Kalman Filter in C++

Implementing Extended Kalman Filter in C++

Utilize an Unscented Kalman Filter to estimate the state of a moving object of interest with noisy Lidar and Radar measurements

Implemented sensor fusion algorithm using an extended Kalman filter that tracks nearby moving objects using RADAR and LIDAR measurements.

Extended Kalman Filter on LiDAR and Radar sensor feed

Implementing a Bicycle tracker with Extended Kalman Filters in C++ for a self-driving car

Unscented Kalman Filter to estimate the state of multiple cars on a simulated highway environment using noisy lidar and radar measurements

An Extended Kalman Filter Project

In this project we will utilize a kalman filter to estimate the state of a moving object of interest with noisy LIDAR and radar measurements.