Research works on the two topics of Semantic Segmentation and SLAM (Simultaneous Localization and Mapping) have been following separate tracks. Here, we link them quite tightly by delineating a category label fusion technique that allows for embedding semantic information into the dense map created by a volume-based SLAM algorithm such as KinectFusion. Accordingly, our approach is the first to provide a semantically labeled dense reconstruction of the environment from a stream of RGB-D images. We validate our proposal using a publicly available semantically annotated RGB-D dataset and (a) employing ground truth labels, (b) corrupting such annotations with synthetic noise, (c) deploying a state of the art semantic segmentation algorithm based on Convolutional Neural Networks.

Volume-based semantic labeling with signed distance functions / Cavallari, Tommaso; Di Stefano, Luigi. - ELETTRONICO. - 9431:(2016), pp. 544-556. (Intervento presentato al convegno 7th Pacific-Rim Symposium on Image and Video Technology, PSIVT 2015 tenutosi a nzl nel 2015) [10.1007/978-3-319-29451-3_43].

Volume-based semantic labeling with signed distance functions

CAVALLARI, TOMMASO;DI STEFANO, LUIGI
2016

Abstract

Research works on the two topics of Semantic Segmentation and SLAM (Simultaneous Localization and Mapping) have been following separate tracks. Here, we link them quite tightly by delineating a category label fusion technique that allows for embedding semantic information into the dense map created by a volume-based SLAM algorithm such as KinectFusion. Accordingly, our approach is the first to provide a semantically labeled dense reconstruction of the environment from a stream of RGB-D images. We validate our proposal using a publicly available semantically annotated RGB-D dataset and (a) employing ground truth labels, (b) corrupting such annotations with synthetic noise, (c) deploying a state of the art semantic segmentation algorithm based on Convolutional Neural Networks.
2016
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
544
556
Volume-based semantic labeling with signed distance functions / Cavallari, Tommaso; Di Stefano, Luigi. - ELETTRONICO. - 9431:(2016), pp. 544-556. (Intervento presentato al convegno 7th Pacific-Rim Symposium on Image and Video Technology, PSIVT 2015 tenutosi a nzl nel 2015) [10.1007/978-3-319-29451-3_43].
Cavallari, Tommaso; Di Stefano, Luigi
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11585/555675
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