Unscented Kalman Filter

A sensor data processing pipeline to estimate the state of a moving object

INTRODUCTION

The goal of this project is to write an Unscented Kalman Filter to estimate the state of a moving object using noisy lidar and radar measurements. To predict the location of the object, a Constant Turn Rate and Velocity (CTRV) model is used.

This project was undertaken as part of the Udacity Self-Driving Car NanoDegree.

Pipeline summary

The Unscented Kalman Filter (UKF), like the Extended Kalman Filter, is a continuous loop of measurement updates and predictions. The UKF deals with non-linearity differently to the EKF, by mapping selected points in the probability distribution graph (sigma points) through the non-linear measurement or non-linear process function, and then calculating the mean state vector and covariance matrix to produce a best-fit linear (Gaussian) representation of the location of the object.

The CTRV model predicts new (x,y) coordinates and turning angle using the assumption of a constant velocity and a constant turn rate (angular velocity).

HOW TO USE

Project dependencies

You can follow the guide in the README of the original project repo.

• https://github.com/udacity/CarND-Unscented-Kalman-Filter-Project

RELEVANT FILES

• ukf.cpp
  • Initialises the state parameters and implements the UKF equations for prediction and radar measurements