Distributed and collaborative monocular simultaneous localization and mapping for multi-robot systems in large-scale environments

In this article, we propose a distributed and collaborative monocular simultaneous localization and mapping system for the multi-robot system in large-scale environments, where monocular vision is the only exteroceptive sensor. Each robot estimates its pose and reconstructs the environment simultaneously using the same monocular simultaneous localization and mapping algorithm. Meanwhile, they share the results of their incremental maps by streaming keyframes through the robot operating system messages and the wireless network. Subsequently, each robot in the group can obtain the global map with high efficiency. To build the collaborative simultaneous localization and mapping architecture, two novel approaches are proposed. One is a robust relocalization method based on active loop closure, and the other is a vision-based multi-robot relative pose estimating and map merging method. The former is used to solve the problem of tracking failures when robots carry out long-term monocular simultaneous localization and mapping in large-scale environments, while the latter uses the appearance-based place recognition method to determine multi-robot relative poses and build the large-scale global map by merging each robot's local map. Both KITTI data set and our own data set acquired by a handheld camera are used to evaluate the proposed system. Experimental results show that the proposed distributed multi-robot collaborative monocular simultaneous localization and mapping system can be used in both indoor small-scale and outdoor large-scale environments.