Real-time and scalable incremental segmentation on dense SLAM
Keisuke Tateno, Federico Tombari, Nassir Navab
Abstract
This work proposes a real-time segmentation method for 3D point clouds obtained via Simultaneous Localization And Mapping (SLAM). The proposed method incrementally merges segments obtained from each input depth image in a unified global model using a SLAM framework. Differently from all other approaches, our method is able to yield segmentation of scenes reconstructed from multiple views in real-time, with a complexity that does not depend on the size of the global model. At the same time, it is also general, as it can be deployed with any frame-wise segmentation approach as well as any SLAM algorithm. We validate our proposal by a comparison with the state of the art in terms of computational efficiency and accuracy on a benchmark dataset, as well as by showing how our method can enable real-time segmentation from reconstructions of diverse real indoor environments.
BibTeX
@inproceedings{iros2015_realtimeandscala,
title = {Real-time and scalable incremental segmentation on dense SLAM},
author = {Keisuke Tateno and Federico Tombari and Nassir Navab},
booktitle = {IROS 2015},
year = {2015}
}