Monocular Ⅵ-SLAM Algorithm Based on Lightweight SuperPoint Network in Low-Light Environment
Visual inertial simultaneous localization and mapping(SLAM)technology improves the accuracy of mapping and positioning by considering relevant visual and inertial constraints.However,in low-light environments,the quality of feature point extraction and tracking stability at the visual front-end are poor,which leads to easy loss of tracking and low positioning accuracy in the visual inertial SLAM algorithm.Therefore,we propose a monocular inertial SLAM algorithm called GS-VINS based on the VINS-Mono framework.First,an adaptive image enhancement algorithm is used to enhance the grayscale distribution of low-light images.Then,a GN2_SuperPoint feature point detection network is proposed,and it is combined with a feature point dynamic tracking module to improve the stability of optical flow tracking.Experiments on the EuRoC dataset and in real-world scenarios show that the proposed algorithm improves localization accuracy by 26.57%compared to VINS-Mono and it demonstrates strong robustness to changes in lighting.In the comparison experiment,the success rate of the feature tracking increases by 8%,and the closure error in real-world scenarios is reduced by～45.73%.The proposed algorithm shows good accuracy and stability in low-light environments and provides a novel solution for visual navigation under low-light conditions,thereby offering valuable engineering applications.
simultaneous localization and mappingvisual inertial systemlow-light scenariooptical trackingpose estimation
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