Towards spatio-temporally consistent semantic mapping

Abstract

Intelligent robots require a semantic map of the surroundings for applications such as navigation and object localization. In order to generate the semantic map, previous works mainly focus on the semantic segmentations on the single RGB-D images and fuse the results by a simple majority vote. However, single image based semantic segmentation algorithms are prone to producing inconsistent segments. Little attentions are paid to the consistency over the semantic map. We present a spatio-temporally consistent semantic mapping approach which can generate the temporal consistent segmentations and enforce the spatial consistency by Dense CRF model. We compare our temporal consistent segment algorithm with the state-of-art approach and generate our semantic map on the NYU v2 dataset.