  To enhance the performance and effect of AR/VR applications and visual
assistance 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 dense
depth information. To overcome these limitations, this paper proposes a
360ORB-SLAM system for panoramic images that combines with a depth completion
network. The system extracts feature points from the panoramic image, utilizes
a panoramic triangulation module to generate sparse depth information, and
employs a depth completion network to obtain a dense panoramic depth map.
Experimental results on our novel panoramic dataset constructed based on Carla
demonstrate that the proposed method achieves superior scale accuracy compared
to existing monocular SLAM methods and effectively addresses the challenges of
feature association and scale ambiguity. The integration of the depth
completion network enhances system stability and mitigates the impact of
dynamic elements on SLAM performance.
