ICP covariance estimation for the localization of a differential robot using odometry and laser scan

Abstract

In this work we present a probabilistic method to solve the localization problem of a differential robot. The Extended Kalman Filter (EKF) is used to merge the information obtained from ICP (Iterative Closest Point) laser measurement records with the odometry information provided by encoders. To use EKF it is necessary to estimate the covariance of each information source, however the ICP algorithm does not return the associated covariance. This paper describes a way to calculate this covariance. The results obtained show that the sensor fusion method results in a more accurate estimation of the robot's pose compared to the estimations obtained through odometry and ICP individually.