  A swarm of robots has advantages over a single robot, since it can explore
larger areas much faster and is more robust to single-point failures. Accurate
relative positioning is necessary to successfully carry out a collaborative
mission without collisions. When Visual Simultaneous Localization and Mapping
(VSLAM) is used to estimate the poses of each robot, inter-agent loop closing
is widely applied to reduce the relative positioning errors. This technique can
mitigate errors using the feature points commonly observed by different robots.
However, it requires significant computing and communication capabilities to
detect inter-agent loops, and to process the data transmitted by multiple
agents. In this paper, we propose Collaborative SLAM using Visual Odometry and
Range measurements (CoVOR-SLAM) to overcome this challenge. In the framework of
CoVOR-SLAM, robots only need to exchange pose estimates, covariances
(uncertainty) of the estimates, and range measurements between robots. Since
CoVOR-SLAM does not require to associate visual features and map points
observed by different agents, the computational and communication loads are
significantly reduced. The required range measurements can be obtained using
pilot signals of the communication system, without requiring complex additional
infrastructure. We tested CoVOR-SLAM using real images as well as real
ultra-wideband-based ranges obtained with two rovers. In addition, CoVOR-SLAM
is evaluated with a larger scale multi-agent setup exploiting public image
datasets and ranges generated using a realistic simulation. The results show
that CoVOR-SLAM can accurately estimate the robots' poses, requiring much less
computational power and communication capabilities than the inter-agent loop
closing technique.
