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.
Cavallari, T., Di Stefano, L. (2016). Volume-based semantic labeling with signed distance functions. Springer Verlag [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.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
