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About this paper
Tianchen Deng, Guole Shen, Xun Chen, Shenghai Yuan, Hongming Shen, Guohao Peng, Zhenyu Wu, Jingchuan Wang and 4 more
MCN-SLAM introduces a distributed multi-agent neural SLAM framework, leveraging a hybrid implicit neural scene representation for efficient, globally consistent 3D reconstruction and localization in large-scale, bandwidth-constrained environments. The system achieved competitive performance in scene reconstruction and camera tracking on various datasets, including a new real-world Dense SLAM (DES) dataset, demonstrating up to 7-fold communication efficiency improvements.
Key insights
- A hybrid implicit neural representation, leveraging tri-plane, hash grid, and coordinate encoding, offers a balanced approach for accurate and scalable scene modeling in diverse environments.
- Integrating intra- and inter-agent loop closures with distributed pose graph optimization is crucial for maintaining global consistency and mitigating pose drift in multi-agent systems.
- Online distillation of network parameters for submap fusion enables efficient and consistent merging of scene representations across collaborating agents with reduced communication overhead.
Summary by alphaXiv.
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