The mobile air cleaner analyzes a distribution of polluted air to purify the air fast, calculates an optimal position and moves the position so that it can remove indoor dust more quickly. At this time, the self-localization is necessary to make the mobile air cleaner move accurately to the calculated position. This paper proposed a self-localization algorithm having high accuracy without complicated calculation and implemented it by combining a ultrasonic sensor and video processing technologies, so that it is suitable for a mobile air cleaner, and as a test result, the mean error of $\pm1cm$ appeared between the actually measured position and the calculated position.
'Keeper of indoor air– Air Cleaner,' House full of Happiness, no.7, pp. 11, 2009
Sang-Yong Rhee, Young-Baek Kim and Jin-Hee Cho, 'A Mobile Air Cleaner to Clean Dust Rapidly,' International Symposium on Robotics, pp. 948-951, 2008
H. Choset and K. Nagatani, 'Topological SLAM toward exact localization without explicit localization,' IEEE Trans. on Robotics and Automation, vol. 17, no.2, pp. 125-137, 2001
S. Thrun, D. Fox, W. Burgard and F. Dellaert, 'Robust monte carlo localization for mobile robots,' Artificial Intelligence, vol. 128, pp. 99-141, 2001
J. D. Tardos, J. Neira, P. M. Newman and J. J. Leonard, 'Robust mapping and localization in indoor environments using sonar data,' International Journal of Robotics Research, vol. 21, no. 4, pp. 311-330, 2002
G. Jang, S. H. Lee and I. Kweon, 'Color landma based self-localization for indoor mobile robots,' Proc. of IEEE Int. Conf. on Robotics and Automation, pp. 1037-1042, 2002
P. Newman, J. Leonard, J. D. Tardos and J. Neira, 'Explore and return : experimental validation of real-time concurrent mapping and localization,' Proc. of IEEE Int. Conf. on Robotics and Automation, pp. 1802-1809, 2002