SF-VIO: a visual-inertial odometry based on selective feature sample using attention mechanism

Qingfeng Zeng; Shumin Chen; Cheng Liu; Shouxu Chen; Yuanxin Xu

doi:10.1117/12.2680973

8 June 2023 SF-VIO: a visual-inertial odometry based on selective feature sample using attention mechanism

Qingfeng Zeng, Shumin Chen, Cheng Liu, Shouxu Chen, Yuanxin Xu

Proceedings Volume 12707, International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2023); 127073E (2023) https://doi.org/10.1117/12.2680973
Event: International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2023), 2023, Changsha, China

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.

Citation Download Citation

Qingfeng Zeng, Shumin Chen, Cheng Liu, Shouxu Chen, and Yuanxin Xu "SF-VIO: a visual-inertial odometry based on selective feature sample using attention mechanism", Proc. SPIE 12707, International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2023), 127073E (8 June 2023); https://doi.org/10.1117/12.2680973

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