A Collaborative Visual SLAM Framework for Service Robots
Ming Ouyang, Xuesong Shi, Yujie Wang, Yuxin Tian, Yingzhe Shen, Dawei Wang, Peng Wang, Zhiqiang Cao
Abstract
We present a collaborative visual simultaneous localization and mapping (SLAM) framework for service robots. With an edge server maintaining a map database and performing global optimization, each robot can register to an existing map, update the map, or build new maps, all with a unified interface and low computation and memory cost. We design an elegant communication pipeline to enable real-time information sharing between robots. With a novel landmark organization and retrieval method on the server, each robot can acquire landmarks predicted to be in its view, to augment its local map. The framework is general enough to support both RGB-D and monocular cameras, as well as robots with multiple cameras, taking the rigid constraints between cameras into consideration. The proposed framework has been fully implemented and verified with public datasets and live experiments.
BibTeX
@inproceedings{iros2021_acollaborativevi,
title = {A Collaborative Visual SLAM Framework for Service Robots},
author = {Ming Ouyang and Xuesong Shi and Yujie Wang and Yuxin Tian and Yingzhe Shen and Dawei Wang and Peng Wang and Zhiqiang Cao},
booktitle = {IROS 2021},
year = {2021}
}