| Summary: | This research presents a novel approach to visual-inertial odometry (VIO) for challenging
environments based on VINS-Fusion. The proposed method utilizes a deep learning technique
to enhance the performance of the state estimation. The proposed approach employs semantic
segmentation to highlight ground features such as lane markings and ground bricks. The exper-
iments’ results demonstrate the proposed method’s effectiveness in improving the robustness
and accuracy of the VIO system in semi-outdoor environments with dynamic objects. The re-
port concludes with a summary of the main findings and recommendations for future research.
This research has the potential to enhance the capabilities of autonomous systems in indoor
environments, such as in factories, hospitals, and shopping centers.
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