VPE-SLAM: Neural Implicit Voxel-permutohedral Encoding for SLAM

摘要

NeRF can reconstruct incredibly realistic environmental maps in dense simultaneous localization and mapping, providing robots with more comprehensive scene map information. However, NeRF often struggles with geometric distortions in indoor reconstructions. To correct geometric distortions, we develop VPE-SLAM, based on the proposed voxel-permutohedral encoding, which can incrementally reconstruct maps of unknown scenes. Specifically, voxel-permutohedral encoding combines a sparse voxel feature grid created by an octree and multi-resolution permutohedral tetrahedral feature grids to represent the scene effectively. Especially when dealing with object edges, our method can effectively encode the geometry and texture of edges by the hybrid structural grid. We propose a novel local bundle adjustment module that utilizes a sliding window mechanism to manage adjacent keyframes requiring optimization. Furthermore, the proposed method establishes local map consistency by repeatedly optimizing keyframes that were initially under-optimized through a compensation strategy. The consistency of the local map can enhance the adaptability of our method to challenging scenes. Extensive experiments demonstrate that our method can achieve accurate camera tracking and produce high-quality reconstruction results on the Replica and ScanNet datasets. The source code will be available at https://github.com/NeuCV-IRMI/VPE-SLAM.