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.

翻译：密集单目SLAM在AR/VR领域具有巨大应用价值，尤其是在移动设备上运行时。本文提出一种创新方法，通过轻型深度补全网络与多基深度表示相结合，将深度补全网络集成至稀疏SLAM系统，实现移动端在线稠密建图。具体而言，我们针对传统稀疏SLAM系统的特性，设计了一种经专门优化的多基深度补全网络BBC-Net。该网络可利用现有关键点SLAM系统生成的稀疏点云，从单目图像中预测多个平衡基与置信度图。最终深度为预测深度基的线性组合，可通过调节相应权重进行优化。为将权重无缝嵌入传统SLAM优化流程并保证效率与鲁棒性，我们设计了一组深度权重因子，使网络成为通用插件模块，可轻松集成至各类现有稀疏SLAM系统，并通过束调整显著提升全局深度一致性。为验证方法可移植性，我们将BBC-Net集成至两个代表性SLAM系统。多数据集实验结果表明，所提方法在单目稠密建图方面优于当前最优方法。我们提供了移动端在线演示，验证了该方法在真实场景中的效率与建图质量。