Dense Point Cloud Mapping by Leveraging Neural-Based Monocular Depth Estimation

Accurate and efficient 3D mapping is essential for applications such as robotics, autonomous navigation, and augmented reality. Traditional mapping methods often rely on expensive depth sensors or sparse monocular Simultaneous Localization and Mapping (SLAM) techniques, both of which face limitations in certain environments. This paper proposes a novel pipeline that integrates neural network-based monocular depth estimation (MDE) into an RGB-D SLAM framework, enabling dense 3D mapping using only RGB images. The pipeline utilizes a neural network to infer depth maps from monocular RGB input, followed by a filtering module to ensure depth consistency based on visual odometry. The resulting dense depth maps are used alongside RGB data to generate detailed point clouds. Experimental evaluations conducted in indoor environments demonstrate that the proposed approach significantly enhances the volumetric density and geometric fidelity of 3D point cloud maps compared to traditional RGB-D SLAM systems having near real-time inference rate. The method also addresses key challenges such as sparsity and limited sensor range, while being modular and easy to adapt for other models and subsystems, laying the foundations for robust and cost-effective mapping solutions. Future work will explore extending the framework to outdoor environments and improving real-time performance as well robustness to out-of-distribution scenarios.