mcarilli / carnd-extended-kalman-filter Goto Github PK

This project implements an extended Kalman filter in C++.

Input data consisting of laser measurements (given directly as x and y positions, with some known uncertainty) and radar measurements (given as radius, angle, and radial velocity relative to some fixed measurement site, with some known uncertainty) are combined with a motion model to track a vehicle with much better accuracy than the individual measurements alone allow.

The code presented here is designed to work with the Udacity term 2 simulation executable, and so cannot be run standalone. However, here's some example output.

Red circles are lidar measurements.

Blue circles are radar measurements (position markers inferred from radius and angle; the also-supplied radial velocity measurements are not shown).

Green markers are the car's position as estimated by the Kalman filter. It's clear that the Kalman filter does a good job of tracking the car's position with significantly reduced noise.