The Journal of Korea Robotics Society (로봇학회논문지)

Korea Robotics Society (한국로봇학회)
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Visual SLAM using Local Bundle Optimization in Unstructured Seafloor Environment

국소 집단 최적화 기법을 적용한 비정형 해저면 환경에서의 비주얼 SLAM

  • Received : 2014.04.23
  • Accepted : 2014.07.16
  • Published : 2014.11.28
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Abstract

As computer vision algorithms are developed on a continuous basis, the visual information from 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. This research addresses a visual SLAM framework for online localization and mapping in an unstructured seabed environment that can be applied to a low-cost unmanned underwater vehicle equipped with a single monocular camera as a major measurement sensor. Typically, an image motion model with a predefined 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 erroneous image motion model, this study employs a local bundle optimization (LBO) scheme when a closed loop is detected. The results of comparison between visual SLAM estimation with LBO and the other case are presented to validate the effectiveness of the proposed methodology.

Keywords

Acknowledgement

Supported by : KAIST

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  1. 사각형 특징 기반 Visual SLAM을 위한 자세 추정 방법 vol.11, pp.1, 2014, https://doi.org/10.7746/jkros.2016.11.1.033