Analysis of an RGB-D Simultaneous Localization and Mapping Algorithm for Unmanned Aerial Vehicle

This study investigates the implementation of an RGB-D Simultaneous Localization and Mapping (SLAM) algorithm on an unmanned aerial vehicle (UAV) equipped with an Intel RealSense D435i camera. The study focuses on Real-Time Appearance-Based Mapping (RTAB-Map), a well-established RGB-D SLAM method capable of building 3D maps while simultaneously localizing a robot within its environment. Despite its advanced capabilities, deploying RTAB-Map on UAVs introduces specific challenges due to the dynamics of aerial navigation. This research evaluates the performance of RTAB-Map in terms of robustness, precision, and accuracy to optimize its application in UAV-based RGB-D SLAM. The findings reveal that the sequential frame-matching approach, combined with a minimum inliers threshold of 10, provides the most robust performance. In contrast, the global matching approach with a minimum inliers threshold of 20 offers better precision and accuracy. The results show that this implementation, utilizing an off-the-shelf hardware and software setup, has significant potential for advanced applications such as monitoring and surveillance in environments, where dense 3D mapping is critical.

@article{Effendi2025,
  title     = {Analysis of an RGB-D Simultaneous Localization and Mapping Algorithm for Unmanned Aerial Vehicle},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {16},
  number    = {8},
  year      = {2025},
  publisher = {The Science and Information Organization},
  author    = {Muhammad Zamir Fathi Mohammad Effendi and Norhidayah Mohamad Yatim and Zarina Mohd Noh and Nur Aqilah Othman},
  doi       = {10.14569/IJACSA.2025.0160861},
  url       = {https://doi.org/10.14569/IJACSA.2025.0160861}
}