Towards Large-Scale Incremental Dense Mapping using Robot-centric Implicit Neural Representation

Large-scale dense mapping is vital in robotics, digital twins, and virtual reality. Recently, implicit neural mapping has shown remarkable reconstruction quality. However, incremental large-scale mapping with implicit neural representations remains problematic due to low efficiency, limited video memory, and the catastrophic forgetting phenomenon. To counter these challenges, we introduce the Robot-centric Implicit Mapping (RIM) technique for large-scale incremental dense mapping. This method employs a hybrid representation, encoding shapes with implicit features via a multi-resolution voxel map and decoding signed distance fields through a shallow MLP. We advocate for a robot-centric local map to boost model training efficiency and curb the catastrophic forgetting issue. A decoupled scalable global map is further developed to archive learned features for reuse and maintain constant video memory consumption. Validation experiments demonstrate our method's exceptional quality, efficiency, and adaptability across diverse scales and scenes over advanced dense mapping methods using range sensors. Our system's code will be accessible at https://github.com/HITSZ-NRSL/RIM.git.