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.

Better Lost in Transition Than Lost in Space: SLAM State Machine A Simultaneous Localization and Mapping (SLAM) system is a complex program consisting of several interconnected components with different functionalities such as optimization, tracking or loop detection. Whereas the literature addresses in detail how enhancing the algorithmic aspects of the individual components improves SLAM performance, the modal aspects, such as when to localize, relocalize or close a loop, are usually left aside. In this paper, we address the modal aspects of a SLAM system and show that the design of the modal controller has a strong impact on SLAM performance in particular in terms of robustness against unforeseen events such as sensor failures, perceptual aliasing or kidnapping. We preset a novel taxonomy for the components of a modern SLAM system, investigate their interplay and propose a highly modular architecture of a generic SLAM system using the Unified Modeling LanguageTM (UML) state machine formalism. The result, called SLAM state machine, is compared to the modal controller of several stateoftheart SLAM systems and evaluated in two experiments. We demonstrate that our state machine handles unforeseen events much more robustly than the stateoftheart systems.