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

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.