In order to achieve real-time operation, existing approaches often assume previously-estimated states to be perfectly known . The Schmidt-Kalman filter has processing cost linear in the size of the state vector but quadratic memory requirements . Inparticular, this method, the resource-aware inverse Schmidt estimator (RISE)allows estimation accuracy for computational efficiency . We evaluate the proposed RISE-SLAMalgorithm on publicly-available datasets and demonstrate its superiority, bothin terms of accuracy and efficiency, as compared to alternative visual-inertialSLAM systems . We also evaluate its superiority on public-available data to demonstrate the superiority, including that of the RISE algorithm, as well as that of other SLAM systems in terms of efficiency and accuracy and that of SLAM

Author(s) : Tong Ke, Kejian J. Wu, Stergios I. Roumeliotis

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Keywords : slam - rise - superiority - efficiency - accuracy -

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