Describing composite urban workspaces
In this paper we present an appearance-based method for augmenting maps of outdoor urban environments with higher-order, semantic labels. Our motivation is to increase the value and utility of the typically low-level representations built by contemporary SLAM algorithms. A supervised learning scheme...
| 主要な著者: | , , , |
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| フォーマット: | Conference item |
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2007
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| 要約: | In this paper we present an appearance-based method for augmenting maps of outdoor urban environments with higher-order, semantic labels. Our motivation is to increase the value and utility of the typically low-level representations built by contemporary SLAM algorithms. A supervised learning scheme is employed to train a set of classifiers to respond to common scene attributes given a mixture of geometric and visual scene information. The union of classifier responses yields a composite description of the local workspace. We apply our method to three large data sets. © 2007 IEEE. |
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