ORBSLAMAtlas: a robust and accurate multimap system We propose ORBSLAMAtlas, a system able to handle an unlimited number of disconnected submaps, that includes a robust map merging algorithm able to detect submaps with common regions and seamlessly fuse them. The outstanding robustness and accuracy of ORBSLAM are due to its ability to detect widebaseline matches between keyframes, and to exploit them by means of nonlinear optimization, however it only can handle a single map. ORBSLAMAtlas brings the widebaseline matching detection and exploitation to the multiple map arena. The result is a SLAM system significantly more general and robust, able to perform multisession mapping. If tracking is lost during exploration, instead of freezing the map, a new submap is launched, and it can be fused with the previous map when common parts are visited. Our criteria to declare the camera lost contrast with previous approaches that simply count the number of tracked points, we propose to discard also inaccurately estimated camera poses due to bad geometrical conditioning. As a result, the map is split into more accurate submaps, that are eventually merged in a more accurate global map, thanks to the multimapping capabilities.We provide extensive experimental validation in the EuRoC datasets, where ORBSLAMAtlas obtains accurate monocular and stereo results in the difficult sequences where ORBSLAM failed. We also build global maps after multiple sessions in the same room, obtaining the best results to date, between 2 and 3 times more accurate than competing multimap approaches. We also show the robustness and capability of our system to deal with dynamic scenes, quantitatively in the EuRoC datasets and qualitatively in a densely populated corridor where camera occlusions and tracking losses are frequent.

SimSLAM 2D: A Simulation Framework for Testing and Benchmarking of twodimensional VisualSLAM Methods Localization proves still a challenging task in robotics, with VisualSLAM being a both powerful and popular solution. In GroundSLAM, a branch of VisualSLAM that uses groundplane views, there is still a lot to explore but the tools for testing new methods need improvement. In this paper, we present a novel framework for simulating the motion of VisualSLAM methods over a given groundplane. It can also be used to benchmark different methods on a given dataset. We present the functioning of our system, as well as the requirements for a similar system to work. In order to showcase the capabilities of SimSLAM 2D, we present a test case of different trajectories on a smooth concrete terrain comparing the performance of two methods, StreetMap and ORBSLAM. We also test StreetMap in localization only mode with the purpose of showing the visualization tools. We thereby show the ease of simulating and testing a VisualSLAM method on a 2D environment in our framework, which represents a useful tool when developing new VisualSLAM methods.