Title :
Gray-dynamic EKF for mobile robot SLAM in indoor environment
Author :
Peng Wang ; Qibin Zhang ; Zonghai Chen
Author_Institution :
Dept. of Autom., Univ. of Sci. & Technol. of China, Hefei, China
Abstract :
The Gray-dynamic EKF (GEKF) algorithm is proposed to estimate the states of a mobile robot in an indoor environment. First, the gray prediction theory is adopted to predict the states of a mobile robot and the feature positions in the environment; next, based on the predictions, a mobile robot system model is built dynamically; then, the GEKF is used to estimate the mobile robot states and the feature positions. Experimental results show that the GEKF can achieve almost the same estimation accuracy with EKF, while without the need of a fixed system model. To improve the head direction estimation accuracy of the mobile robot, a head direction match algorithm is proposed, and relatively better results are shown by experiments.
Keywords :
Kalman filters; SLAM (robots); grey systems; mobile robots; nonlinear filters; position control; prediction theory; robot vision; state estimation; GEKF algorithm; extended Kalman filter; gray prediction theory; gray-dynamic EKF algorithm; head direction estimation accuracy; head direction match algorithm; indoor environment; mobile robot SLAM; simultaneous localization and planning; state estimation; Estimation; Mathematical model; Mobile robots; Predictive models; Simultaneous localization and mapping; gray-dynamic EKF; indoor environment; mobile robot; state estimation;
Conference_Titel :
Robotics, Automation and Mechatronics (RAM), 2013 6th IEEE Conference on
Conference_Location :
Manila
Print_ISBN :
978-1-4799-1198-1
DOI :
10.1109/RAM.2013.6758557