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To enhance the performance and effect of AR/VR applications and visualassistance and inspection systems, visual simultaneous localization and mapping(vSLAM) is a fundamental task in computer vision and robotics. However,traditional vSLAM systems are limited by the camera's narrow field-of-view,resulting in challenges such as sparse feature distribution and lack of densedepth information. To overcome these limitations, this paper proposes a360ORB-SLAM system for panoramic images that combines with a depth completionnetwork. The system extracts feature points from the panoramic image, utilizesa panoramic triangulation module to generate sparse depth information, andemploys a depth completion network to obtain a dense panoramic depth map.Experimental results on our novel panoramic dataset constructed based on Carlademonstrate that the proposed method achieves superior scale accuracy comparedto existing monocular SLAM methods and effectively addresses the challenges offeature association and scale ambiguity. The integration of the depthcompletion network enhances system stability and mitigates the impact ofdynamic elements on SLAM performance.

360ORB-SLAM: A Visual SLAM System for Panoramic Images with Depth Completion Network