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We propose ORBSLAM-Atlas, a system able to handle an unlimited number ofdisconnected sub-maps, that includes a robust map merging algorithm able todetect sub-maps with common regions and seamlessly fuse them. The outstandingrobustness and accuracy of ORBSLAM are due to its ability to detectwide-baseline matches between keyframes, and to exploit them by means ofnon-linear optimization, however it only can handle a single map. ORBSLAM-Atlasbrings the wide-baseline matching detection and exploitation to the multiplemap arena. The result is a SLAM system significantly more general and robust,able to perform multi-session mapping. If tracking is lost during exploration,instead of freezing the map, a new sub-map is launched, and it can be fusedwith the previous map when common parts are visited. Our criteria to declarethe camera lost contrast with previous approaches that simply count the numberof tracked points, we propose to discard also inaccurately estimated cameraposes due to bad geometrical conditioning. As a result, the map is split intomore accurate sub-maps, that are eventually merged in a more accurate globalmap, thanks to the multi-mapping capabilities.  We provide extensive experimental validation in the EuRoC datasets, whereORBSLAM-Atlas obtains accurate monocular and stereo results in the difficultsequences where ORBSLAM failed. We also build global maps after multiplesessions in the same room, obtaining the best results to date, between 2 and 3times more accurate than competing multi-map approaches. We also show therobustness and capability of our system to deal with dynamic scenes,quantitatively in the EuRoC datasets and qualitatively in a densely populatedcorridor where camera occlusions and tracking losses are frequent.

ORBSLAM-Atlas: a robust and accurate multi-map system