Visual Odometry & SLAM Utilizing Indoor Structured Environments
Автор: NAVER D2
Загружено: 2018-11-14
Просмотров: 5960
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발표자: 김표진 (서울대 박사과정)
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발표일: 2018.08
Visual odometry (VO) and simultaneous localization and mapping (SLAM) are fundamental building blocks for various applications from autonomous vehicles to virtual and augmented reality (VR/AR).
To improve the accuracy and robustness of the VO & SLAM approaches, we exploit multiple lines and orthogonal planar features, such as walls, floors, and ceilings, common in man-made indoor environments.
We demonstrate the effectiveness of the proposed VO & SLAM algorithms through an extensive evaluation on a variety of RGB-D datasets and compare with other state-of-the-art methods.
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