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Semantic mapping is a long term goal to understand environment for mobile robots. The indoor environments usually consist of a large amount of planar surfaces. Thus, plane segmentation is an essential prerequisite to build a semantic map. In this paper, we develop an algorithm to segment planar surfaces from noisy point clouds of indoor scenes. The proposed segmentation algorithm is based on a graph-based...
In order to work in realistic scenarios, it is a desirable feature for autonomous robots to extract semantic concepts from environments. In this paper, A Relevance Vector Machine (RVM) based approach is presented for the task of visual semantic place recognition. The high sparsity and Bayesian property makes this approach capable of obtaining probabilistic confidence estimation, and computationally...
We propose a novel semantic map representation method of indoor environment for mobile robots. The semantics is based on objects, which can be a desk, a wall, a room, or other things. The environment model is composed of object identifiers, object properties and relationships among them. The map is represented by Web Ontology Language (OWL) to share semantic knowledge with human. In order to extract...
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