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Background Foreground Segmentation for SLAM
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DOI：10.1109/TITS.2011.2143706.png)
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摘要
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To perform simultaneous localization and mapping (SLAM) in dynamic environments, static background objects must first be determined. This condition can be achieved using a priori information in the form of a map of background objects. Such an approach exhibits a causality dilemma, because such a priori information is the ultimate goal of SLAM. In this paper, we propose a background foreground segmentation method that overcomes this issue. Localization is achieved using a robust iterative closest point implementation and vehicle odometry. Background objects are modeled as objects that are consistently located at a given spatial location. To improve robustness, classification is performed at the object level through the integration of a new segmentation method that is robust to partial object occlusion.
Keyword:
Background-Foreground segmentation
light detection and ranging (LIDAR)
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IF:
8.4
论文数:
9.5K
被引数:
6.3W
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