Collaborative mapping aids agents in achieving an efficient and comprehensive understanding of their environment. Recently, there has been growing interest in using neural networks as maps to represent functions that implicitly define the geometric features of a scene. However, existing implicit mapping algorithms are constrained to single-agent scenarios, thus restricting mapping efficiency. In this letter, we present MACIM , a M ulti- A gent C ollaborative I mplicit M apping algorithm to construct implicit Euclidean Signed Distance Field (ESDF) maps, formulated as a distributed optimization task. In our formulation, each agent independently maintains its own local data and neural network. At each iteration, agents train networks using local data and network weights from their peers. Subsequently, they transmit the latest version of network weights to their neighbors, thus keeping the local network weights of all agents continuously consistent. When optimizing the network model, the agents use not raw but in-grid fused sensor data to prevent training data conflicts. In addition, we constrain the signed distance values of unobserved regions by Small Batch Euclidean Distance Transform (SBEDT) to mitigate reconstruction artifacts. The verification results on multiple scenes demonstrate that MACIM builds more accurate ESDFs and meshes than single-agent strategy and some distributed optimization methods.

中文翻译：

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MACIM：多智能体协作隐式映射

协作测绘可帮助智能体高效、全面地了解其环境。最近，人们越来越感兴趣使用神经网络作为地图来表示隐式定义场景几何特征的函数。然而，现有的隐式映射算法仅限于单代理场景，从而限制了映射效率。在这封信中，我们提出麦卡姆多- 代理人协作性隐含的用于构造隐式欧几里得符号距离场 (ESDF) 映射的映射算法，将其表述为分布式优化任务。在我们的公式中，每个代理独立维护自己的本地数据和神经网络。在每次迭代中，代理使用本地数据和来自其同伴的网络权重来训练网络。随后，它们将最新版本的网络权重传输给邻居，从而保持所有代理的本地网络权重持续一致。在优化网络模型时，代理使用的不是原始数据而是网格内融合的传感器数据，以防止训练数据冲突。此外，我们通过小批量欧几里德距离变换（SBEDT）约束未观察区域的有符号距离值，以减轻重建伪影。多个场景的验证结果表明，MACIM比单代理策略和一些分布式优化方法构建了更准确的ESDF和网格。