Map Completion for SLAM Systems Based on Neural Radiance Field

Simultaneous localization and mapping (SLAM) systems aim to enhance the capabilities of robots in complex environments by building maps of unknown surroundings. Creating a dense and detailed map can significantly improve the robot's perception of the environment and enable it to perform more challenging tasks. However, traditional algorithms often struggle to balance localization, tracking, and mapping. Recently, neural radiance fields-based 3D reconstruction has emerged as an efficient and accurate method, offering a potential solution for map completion in SLAM systems. In our work, we integrate neural radiance fields with the SLAM system, providing a real-time solution for tracking, localization, and initial scene reconstruction, along with offline map completion. Experimental results demonstrate that our approach sacrifices some tracking accuracy to achieve complete map construction.