국소 집단 최적화 기법을 적용한 비정형 해저면환경에서의 비주얼 SLAM

Abstract

As computer vision algorithms are developed on a continuous basis, the visual informationfrom vision sensors has been widely used in the context of simultaneous localization and mapping(SLAM), called visual SLAM, which utilizes relative motion information between images. Thisresearch addresses a visual SLAM framework for online localization and mapping in an unstructuredseabed environment that can be applied to a low-cost unmanned underwater vehicle equipped with asingle monocular camera as a major measurement sensor. Typically, an image motion model with apredefined dimensionality can be corrupted by errors due to the violation of the model assumptions,which may lead to performance degradation of the visual SLAM estimation. To deal with the erroneousimage motion model, this study employs a local bundle optimization (LBO) scheme when a closed loopis detected. The results of comparison between visual SLAM estimation with LBO and the other caseare presented to validate the effectiveness of the proposed methodology