Current study on NeRF-based techniques in SLAM frameworks

Abstract

This paper comprehensively examines the application and current research status of Neural Radiance Fields (NeRF) technology within Simultaneous Localization and Mapping (SLAM) systems. NeRF, which has gained significant attention since 2020, has evolved into a powerful method for reconstructing 3D scenes from images, offering advantages such as continuous scene representation and photorealistic novel view synthesis. However, it also comes with drawbacks, including substantial training data requirements, limited model generalization, and challenges in map scalability. In contrast, SLAM is a complex, real-time, efficient, and robust system capable of tracking camera motion and constructing environmental maps in real-time, with no limitations on map size. The integration of NeRF technology into SLAM enhances the capabilities of various modules, including Mapping, Tracking, Optimization, Loop Closure, and Localization, providing potential advantages. Beginning with an exploration of NeRFs fundamental principles and its inherent strengths and weaknesses, this paper delves into the significant implications of integrating NeRF into the SLAM pipeline. It addresses the challenges encountered during implementation and outlines potential future directions. The aim is to provide a clear elucidation of the evolving landscape of the combined NeRF and SLAM approach, serving as a reference for researchers interested in pursuing this research direction.