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This paper presents an algorithm for visual SLAM based on a visual plane, a reliable grouping of salient visual features along sonar line features. The grouping of visual features improves data association and reduces the number of landmarks against individual visual features. To accomplish this, we propose three techniques: 1) selection of visual features which are invariant to image changes in indoor environment and suitable candidates for the visual plane, 2) extraction of sonar line features with current sensor data, which filters out uncertain outliers efficiently and 3) a scheme on grouping visual features with respect to sonar line features and maintaining database of the extracted visual planes for reliable data association. We integrate above three techniques into one framework and propose a SLAM algorithm for the visual planes. Experimental results in two types of real home environment show that the algorithm can successfully be executed with no human intervention.
Date of Conference: 10-14 April 2007