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.

为了提高 AR/VR 应用和视觉辅助和检测系统的性能和效果，本文提出了一种结合深度补全网络的 360ORB-SLAM 系统，用于全景图像中的视觉同时定位和建图。实验证明，该方法在尺度准确性方面优于现有的单目 SLAM 方法，并有效解决了特征关联和尺度模糊的挑战，深度补全网络的集成提高了系统稳定性，减轻了动态元素对 SLAM 性能的影响。

360ORB-SLAM：全景图深度完成网络的视觉 SLAM 系统

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

Dense SLAM based on monocular cameras does indeed have immense application
value in the field of AR/VR, especially when it is performed on a mobile
device. In this paper, we propose a novel method that integrates a light-weight
depth completion network into a sparse SLAM system using a multi-basis depth
representation, so that dense mapping can be performed online even on a mobile
phone. Specifically, we present a specifically optimized multi-basis depth
completion network, called BBC-Net, tailored to the characteristics of
traditional sparse SLAM systems. BBC-Net can predict multiple balanced bases
and a confidence map from a monocular image with sparse points generated by
off-the-shelf keypoint-based SLAM systems. The final depth is a linear
combination of predicted depth bases that can be optimized by tuning the
corresponding weights. To seamlessly incorporate the weights into traditional
SLAM optimization and ensure efficiency and robustness, we design a set of
depth weight factors, which makes our network a versatile plug-in module,
facilitating easy integration into various existing sparse SLAM systems and
significantly enhancing global depth consistency through bundle adjustment. To
verify the portability of our method, we integrate BBC-Net into two
representative SLAM systems. The experimental results on various datasets show
that the proposed method achieves better performance in monocular dense mapping
than the state-of-the-art methods. We provide an online demo running on a
mobile phone, which verifies the efficiency and mapping quality of the proposed
method in real-world scenarios.

提出了一种将轻量级深度完成网络集成到稀疏 SLAM 系统中的新方法，使得即使在移动手机上也可以在线进行稠密建图，具有广泛的 AR/VR 应用价值。通过对多尺度深度表示进行优化，通过 BBC-Net 网络对稀疏点云进行多平衡基和置信度图的预测，最终的深度是通过线性组合权重进行优化，同时设计了一组深度权重因子以确保效率和鲁棒性，并将该方法集成到两个代表性 SLAM 系统中，实验证明了该方法在单目稠密建图方面的优越性。