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- W4379792014 abstract "Visual-inertial odometry is widely used in robot motion estimation due to its excellent performance in dealing with scale ambiguity. In this paper, an end-to-end monocular visual inertial odometry is proposed, and its innovation lies in its feature selectivity to extract key features to estimate motion, hence we call it SF-VIO. The most significant difference between our proposed SF-VIO and other methods is that SF-VIO uses key features to estimate robot motion more accurately after feature selection. The feature selection is divided into two steps. Firstly, SF-VIO implements the Attention Mechanism in the image process module Locator to filter useless image information. Secondly, SF-VIO implements a soft fusion mechanism in the feature fusion module Fusion, which effectively integrates visual features and inertial features. The experimental results on the KITTI dataset show that the SF-VIO algorithm outperforms similar methods such as DeepVO and ESP-VO in accuracy." @default.
- W4379792014 created "2023-06-09" @default.
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- W4379792014 date "2023-06-08" @default.
- W4379792014 modified "2023-09-23" @default.
- W4379792014 title "SF-VIO: a visual-inertial odometry based on selective feature sample using attention mechanism" @default.
- W4379792014 doi "https://doi.org/10.1117/12.2680973" @default.
- W4379792014 hasPublicationYear "2023" @default.
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