02. Intro to Extended Kalman Filter Project

Project Introduction

Now that you have learned how the extended Kalman filter works, you are going to implement the extended Kalman filter in C++. We are providing simulated lidar and radar measurements detecting a bicycle that travels around your vehicle. You will use a Kalman filter, lidar measurements and radar measurements to track the bicycle's position and velocity.

The first step is to download the simulator, which contains all the projects for Self-Driving Car Nanodegree. More detailed instruction about setting up the simulator with uWebSocketIO can be found at the end of this section.

Lidar measurements are red circles, radar measurements are blue circles with an arrow pointing in the direction of the observed angle, and estimation markers are green triangles. The video below shows what the simulator looks like when a c++ script is using its Kalman filter to track the object. The simulator provides the script the measured data (either lidar or radar), and the script feeds back the measured estimation marker, and RMSE values from its Kalman filter.

Example of Tracking with Lidar

Check out the video below to see a real world example of object tracking with lidar. In this project, you will only be tracking one object, but the video will give you a sense for how object tracking with lidar works: