Dynamic triangulation for mobile robot localization using an angular state Kalman filter

Abstract

Localization is one of the fundamental problems in mobile robot navigation. Several approaches to cope with the dynamic positioning problem have been made. Most of them use an extended Kalman filter (EKF) to estimate the robot configuration –position and orientation– fusing both the robot odometry and external measurements. In this paper, an EKF is applied to estimate the angles, relative to the robot frame, of the straight lines from a rotating laser scanner to a set of landmarks. By means of these angles, triangulation can be consistently applied at any time to determine the robot configuration. The method is robust to outliers because an expected value of each landmark angle is determined at any time. Simulation and experimental results showing the accuracy of this method are presented.