An Efficient And Robust Framework For Collaborative Monocular Visual Slam

Abstract

Visual SLAM (VSLAM) has shown remarkable performance in robot navigation and its practical applicability can be enriched by building a multi-robot collaboration framework called Visual collaborative SLAM (CoSLAM). CoSLAM extends the usage of SLAM for navigating in larger areas for certain applications like inspection etc. using multiple vehicles which not only saves time but also power. Visual CoSLAM framework suffers from problems like i) Robot can start from anywhere in the scene using their own VSLAM which save both time and power ii) making the framework independent of the choice of SLAM for greater applicability of different SLAMs, iii) avoiding collision with other robots by a robust merging of two noisy maps, when the visual overlap is detected. Very few works are available in the literature which addresses the above problems in a single framework in a practical sense. In this paper, we present a framework for CoSLAM using monocular cameras addressing all the above problems. Unlike existing systems which work only on ORB SLAM, our framework is truly independent of SLAMs. We propose a deep learning based algorithm to find out the visually overlapped scene required for merging two or more 3D maps. Our Map Merging is robust in presence of outliers as we compute similarity transforms using both structural information as well as camera-camera relationships and choose one based on a statistical inference. Experimental results show that our framework is robust and works well for any individual SLAM where we demonstrate our result on ORB and EdgeSLAM which are prototypical extremes methods for map merging in a CoSLAM framework.