로봇의 자율주행을 위한 칼만필터 위치 추정 방법

Abstract

This paper reports Kalman filter methods for localization of robots. The methods are implemented for robots in indoor and underwater environment. They propose a new model of measurement uncertainty which adjusts the error covariance depending on the measured distance. The methods also use non-zero off-diagonal values in error covariance matrices of motion uncertainty and measurement uncertainty. The method is tested through experiments in an indoor environment of 100*40 m working space using a differential drive robot which uses Laser range finder as an exteroceptive sensor. The results compare the localization performance of the proposed method with the conventional method which doesn’t use adaptive measurement uncertainty model. Also, the experiment verifies the improvement due to non-zero off-diagonal elements in covariance matrices. This paper contributes to implementing and evaluating a practical UKF and EKF approach for mobile robot localization. This paper also includes a simulation method to generate sensor measurements for location estimation of an underwater robot. Field trial of a navigation method of an underwater robot takes much time and expenses and it is difficult to change the environment of the field trial as desired to test the method in various situations. Therefore, test and verification of a navigation method through simulation is inevitable for underwater environment. This paper proposes a method to generate sensor measurements of range, depth, velocity, and attitude taking the uncertainties of measurements into account through simulation. The uncertainties are Gaussian noise, outlier, and correlation between the measurement noises. Also, the method implements uncertainty in sampling time of measurements. The method is tested and verified by comparing the uncertainty parameters calculated statistically from the generated measurements with the designed uncertainty parameters. The practical feasibility of the measurement data is shown by applying the measurement data for location estimation of an underwater robot.