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.

翻译：为了增强AR/VR应用以及视觉辅助与检测系统的性能与效果，视觉同步定位与地图构建（vSLAM）是计算机视觉与机器人学中的一项基础任务。然而，传统vSLAM系统受限于相机窄视野，导致特征分布稀疏及缺乏密集深度信息等挑战。为克服这些局限，本文提出一种面向全景图像的360ORB-SLAM系统，该系统结合了深度补全网络。系统从全景图像中提取特征点，利用全景三角测量模块生成稀疏深度信息，并通过深度补全网络获取密集的全景深度图。在基于Carla构建的新型全景数据集上的实验结果表明，与现有单目SLAM方法相比，所提方法在尺度精度上表现更优，有效解决了特征关联与尺度模糊性挑战。深度补全网络的集成增强了系统稳定性，并减轻了动态元素对SLAM性能的影响。