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...
| Main Authors: | , , , |
|---|---|
| 格式: | Conference item |
| 出版: |
2007
|
| 总结: | 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. |
|---|