NISB-Fusion: Multi-Agent Mapping and Map Merging With Neural Implicit Spatial Block
Beichen Xiang, Shichao Zhou, Chengjie Gu, Zhongqu Xie, Linkun Chen, Yulin Wang
Abstract
Recent advancements have demonstrated the potential of radiance representations for high-quality mapping and reconstruction. However, these methods face significant challenges in large-scale, multi-agent scenarios, particularly in terms of computational demands and transmission bandwidth requirements. To address these limitations, we propose a NeRF-based framework designed for multi-agent mapping in large-scale indoor environments. Our approach leverages Neural Implicit Spatial Blocks (NISB) to dynamically cover the scene during mapping. Descriptors extracted from NISB and their associated keyframes are used for top-down place recognition and hybrid submap merging, enabling effective integration. Experimental results show that our method outperforms existing implicit multi-agent mapping approach, delivering superior efficiency and accuracy.
BibTeX
@inproceedings{ral2025_nisbfusionmultia,
title = {NISB-Fusion: Multi-Agent Mapping and Map Merging With Neural Implicit Spatial Block},
author = {Beichen Xiang and Shichao Zhou and Chengjie Gu and Zhongqu Xie and Linkun Chen and Yulin Wang},
booktitle = {RA-L 2025},
year = {2025}
}