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Background Foreground Segmentation for SLAM

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4 Author(s)
Corcoran, P. ; Dept. of Comput. Sci., Nat. Univ. of Ireland, Maynooth, Ireland ; Winstanley, A. ; Mooney, P. ; Middleton, R.

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.

Published in:

Intelligent Transportation Systems, IEEE Transactions on  (Volume:12 ,  Issue: 4 )