# Vision-Guided Autonomous Robot Navigation in Realistic 3D Dynamic Scenarios +
## 3 Pipeline for 3D Mesh Reconstruction
### Sparse and Dense Maps  
The original source code of ORB-SLAM3, available on GitHub ([[https://github.com/UZ-SLAMlab/ORB_SLAM3](https://github.com/UZ-SLAMlab/ORB_SLAM3)]([https://github.com/UZ-SLAMlab/ORB_SLAM3](https://github.com/UZ-SLAMlab/ORB_SLAM3)), accessed on 1 March 2023), is based on a sparse map architecture referred to as the Atlas. To enhance its capabilities for storing and retrieving maps, we introduced several modifications. These adjustments allow the pre-built map to play a critical role in enabling real-time relocalization for navigation tasks. The sparse map is stored as a binary file with the extension *.osa. By integrating this pre-built Atlas into ORB-SLAM3, the system achieves reliable relocalization, even under subtle motions such as minor self-rotations of a mobile robot, thus effectively addressing the challenges posed by diverse indoor and outdoor environments.  
To ensure the accuracy of the 3D map, we downsampled the depth images and aligned them using the poses calculated by the SLAM algorithm. Following the global reconstruction, point cloud filtering was applied to obtain a refined and usable 3D reconstruction [25].  
In contrast to the sparse maps generated natively by ORB-SLAM3, we incorporated dense mapping into our framework by seamlessly integrating SurfelMeshing [26] into the real-time 3D reconstruction pipeline. SurfelMeshing is a well-established technique for online surfel-based mesh reconstruction, recognized for its versatility in real-time 3D reconstruction and rendering across extensive environments. The process comprises four primary stages: surfel reconstruction, surfel denoising, meshing, and remeshing.  
The first stage involves generating an unstructured collection of surfels that represent visible surfaces, constructed from depth data acquired by RGB-D cameras. These surfels are then subjected to a rigorous regularization and blending process to reduce noise and address discontinuities at surface boundaries, thereby enhancing surfel denoising. The meshing phase follows, incorporating detailed neighbor searches and projecting the central surfel onto its tangent plane to construct a coherent mesh. Finally, the remeshing stage systematically removes outdated surfels and establishes new connections with neighboring elements, ensuring the continuity and accuracy of the reconstructed model.  
SurfelMeshing provides numerous inherent advantages over traditional mesh reconstruction methodologies, establishing itself as a critical tool in the domains of real-time 3D mapping, autonomous navigation, and virtual reality. In our experiments, we utilize a depth camera to efficiently capture RGB-D data. In the subsequent sections of this paper, we present two illustrative case studies: one focusing on an indoor scenario and the other on an outdoor context. Our pipeline operates seamlessly, delivering high-quality 3D mapping results, accurately captured as the mobile robot navigates through dynamic environments. These results are vividly depicted in Figures 5-8.  
Figure 5: The sparse map constructed outside the Building of Engineering Building at National Taiwan University (NTU).