Depth Completion with Multiple Balanced Bases and Confidence for Dense Monocular SLAM

TL;DR

This paper introduces BBC-Net, a lightweight depth completion network that enhances monocular SLAM systems with dense mapping capabilities on mobile devices by predicting multiple depth bases and confidence maps for improved accuracy.

Contribution

The paper presents a novel multi-basis depth completion network, BBC-Net, optimized for integration with sparse SLAM systems to enable real-time dense mapping on mobile devices.

Findings

BBC-Net outperforms state-of-the-art methods in dense mapping accuracy.

The method is effectively integrated into existing SLAM systems, improving global depth consistency.

Real-world mobile demo demonstrates efficiency and high-quality dense mapping.

Abstract

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.