Region sampling NeRF-SLAM based on Kolmogorov-Arnold network

Abstract

Currently, NeRF-based SLAM is rapidly developing in reconstructing and bitwise estimating indoor scenes. Compared with traditional SLAM, the advantage of the NeRF-based approach is that the error returns to the pixel itself, the optimization process is WYSIWYG, and it can also be differentiated for map representation. Still, it is limited by its MLP-based implicit representation to scale to larger and more complex environments. Inspired by the quadtree in ORB-SLAM2 and the recently proposed Kolmogorov-Arnold network, our approach replaces the MLP with a KAN network based on Gaussian functions, combines quadtree-based regional pixel sampling and random sampling, delineates the scene by voxels, and supports dynamic scaling to realize a high-fidelity reconstruction of large scenes for a SLAM system. Exposure compensation and VIT loss are also introduced to alleviate the necessity of NeRF on dense coverage, which significantly improves the ability to reconstruct sparse outdoor view environments stable. Experiments on three different types of datasets show that our approach reduces the trajectory error accuracy of indoor datasets from centimeter-level to millimeter-level compared to existing NeRF-based SLAM and achieves stable reconstruction in complex outdoor environments, considering the performance while ensuring efficiency.