RISESLAM: A Resourceaware Inverse Schmidt Estimator for SLAM In this paper, we present the RISESLAM algorithm for performing visualinertial simultaneous localization and mapping (SLAM), while improving estimation consistency. Specifically, in order to achieve realtime operation, existing approaches often assume previouslyestimated states to be perfectly known, which leads to inconsistent estimates. Instead, based on the idea of the SchmidtKalman filter, which has processing cost linear in the size of the state vector but quadratic memory requirements, we derive a new consistent approximate method in the information domain, which has linear memory requirements and adjustable (constant to linear) processing cost. In particular, this method, the resourceaware inverse Schmidt estimator (RISE), allows trading estimation accuracy for computational efficiency. Furthermore, and in order to better address the requirements of a SLAM system during an exploration vs. a relocalization phase, we employ different configurations of RISE (in terms of the number and order of states updated) to maximize accuracy while preserving efficiency. Lastly, we evaluate the proposed RISESLAM algorithm on publiclyavailable datasets and demonstrate its superiority, both in terms of accuracy and efficiency, as compared to alternative visualinertial SLAM systems.

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.