NISB-Map: Scalable Mapping with Neural Implicit Spatial Block

Recently, neural implicit representations have been applied in the mapping process of simultaneous localization and mapping (SLAM), accompanied by less storage overhead and continuous representation. Nevertheless, related methods use a single neural network to represent the whole scene, resulting in forgetting the observed regions caused by the limited capacity of a single network in the large-scale scene. Several methods encode the scene into implicit voxels to avoid parameter forgetting while the memory is sacrificed. In this letter, we introduce a scalable mapping framework that utilizes extensible Neural Implicit Spatial Blocks (NISB) with fixed size to cover the entire scene by incrementally creating multiple Multi-Layer Perceptron (MLP) networks. In evaluations against alternative methods on 3 datasets of indoor environments, our method Avoids forgetting the observed areas during the mapping process with a small memory footprint and smoothly updates the global map at 2 Hz.