Multi-agent collaborative incremental implicit mapping based on topological communication protocol

Multi-agent collaborative mapping technology holds significant application value within the domain of simultaneous localization and mapping. Currently, achieving a comprehensive representation of complex scenes while efficiently ensuring global consensus among multiple agents remain a formidable challenge. In recent years, implicit scene representation, characterized by its lightweight structure, has been employed in mapping tasks. Nevertheless, existing methods are predominantly constrained to single-agent scenarios, and fail to adequately address specific issues such as geometric detail loss, reconstruction artifacts, and inadequate global collaboration support. Consequently, a novel multi-agent collaborative implicit mapping model, termed Topo-Map, is proposed, which is underpinned by a topological communication protocol. Specifically, Topo-Map integrates a hierarchical sampling strategy coupled with a surface-aware adaptive grid optimization method, enabling a screening mechanism for fuzzy grids and the establishment of prior constraints. The demands for hole filling and artifact suppression are optimally met by this dual strategy. Meanwhile, an adaptive enhanced feature aggregation module is incorporated, which is engineered to capture muti-scale features. Leveraging upon this foundation, the consensus alternating direction multiplier method is implemented within a neighborhood topological communication graph, constructed through a gaussian soft neighbor heuristic mechanism, thereby achieving information sharing and gradient synchronization. Furthermore, a parameter consistency loss is introduced to constrain weight disparities, ensuring global mapping consensus while preserving the local autonomy of individual agents. Experimental results demonstrate that Topo-Map achieves enhanced stability with notable robustness and performance gains in collaborative implicit mapping, offering a scalable solution for SLAM in intricate scenes.

Authors

Institutions

Publication Details

Journal
Complex & Intelligent Systems
Published
2026-01-07
DOI
https://doi.org/10.1007/s40747-025-02214-y
Primary Topic
Robotics and Sensor-Based Localization
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Multi-agent collaborative incremental implicit mapping based on topological communication protocol

Pan Li, Zhigong Song, Yupeng Zheng
Complex & Intelligent Systems
Robotics and Sensor-Based Localization
article

Multi-agent collaborative incremental implicit mapping based on topological communication protocol

Pan Li, Zhigong Song, Yupeng Zheng
article en

Abstract

Multi-agent collaborative mapping technology holds significant application value within the domain of simultaneous localization and mapping. Currently, achieving a comprehensive representation of complex scenes while efficiently ensuring global consensus among multiple agents remain a formidable challenge. In recent years, implicit scene representation, characterized by its lightweight structure, has been employed in mapping tasks. Nevertheless, existing methods are predominantly constrained to single-agent scenarios, and fail to adequately address specific issues such as geometric detail loss, reconstruction artifacts, and inadequate global collaboration support. Consequently, a novel multi-agent collaborative implicit mapping model, termed Topo-Map, is proposed, which is underpinned by a topological communication protocol. Specifically, Topo-Map integrates a hierarchical sampling strategy coupled with a surface-aware adaptive grid optimization method, enabling a screening mechanism for fuzzy grids and the establishment of prior constraints. The demands for hole filling and artifact suppression are optimally met by this dual strategy. Meanwhile, an adaptive enhanced feature aggregation module is incorporated, which is engineered to capture muti-scale features. Leveraging upon this foundation, the consensus alternating direction multiplier method is implemented within a neighborhood topological communication graph, constructed through a gaussian soft neighbor heuristic mechanism, thereby achieving information sharing and gradient synchronization. Furthermore, a parameter consistency loss is introduced to constrain weight disparities, ensuring global mapping consensus while preserving the local autonomy of individual agents. Experimental results demonstrate that Topo-Map achieves enhanced stability with notable robustness and performance gains in collaborative implicit mapping, offering a scalable solution for SLAM in intricate scenes.

Complex & Intelligent SystemsVol. 12(3)
Jiangnan University (CN)
Openalex Percentile: Top 96%
Robotics and Sensor-Based Localization
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.