  This paper proposes a novel simultaneous localization and mapping (SLAM)
approach, namely Attention-SLAM, which simulates human navigation mode by
combining a visual saliency model (SalNavNet) with traditional monocular visual
SLAM. Most SLAM methods treat all the features extracted from the images as
equal importance during the optimization process. However, the salient feature
points in scenes have more significant influence during the human navigation
process. Therefore, we first propose a visual saliency model called SalVavNet
in which we introduce a correlation module and propose an adaptive Exponential
Moving Average (EMA) module. These modules mitigate the center bias to enable
the saliency maps generated by SalNavNet to pay more attention to the same
salient object. Moreover, the saliency maps simulate the human behavior for the
refinement of SLAM results. The feature points extracted from the salient
regions have greater importance in optimization process. We add semantic
saliency information to the Euroc dataset to generate an open-source saliency
SLAM dataset. Comprehensive test results prove that Attention-SLAM outperforms
benchmarks such as Direct Sparse Odometry (DSO), ORB-SLAM, and Salient DSO in
terms of efficiency, accuracy, and robustness in most test cases.
