多机器人制图的分布式高斯过程模型
A Distributed Gaussian Process Model for Multi-Robot Mapping
AI总结：
DistGP通过分布式高斯过程实现多机器人协同制图，优于现有方法并在动态环境下表现更优。
摘要：
我们提出了DistGP：一种多机器人学习方法，用于通过仅本地经验和计算进行协同学习全局函数。我们利用一种稀疏高斯过程（GP）模型，其分解方式与任务的多机器人结构相匹配，并允许通过高斯信念传播（GBP）进行分布式训练。我们的环形模型优于树状高斯过程（Tree-Structured GPs）\cite{bui2014tree}，并且可以在动态连接设置中在线训练。我们证明，这种分布式、异步训练可以达到集中式、批量训练模型的相同性能，尽管收敛速度较慢。最后，我们与DiNNO \cite{yu2022dinno}，一种分布式神经网络（NN）优化器进行比较，发现DistGP在准确性上更优，对稀疏通信更鲁棒，并且更能持续学习。
英文摘要：
We propose DistGP: a multi-robot learning method for collaborative learning of a global function using only local experience and computation. We utilise a sparse Gaussian process (GP) model with a factorisation that mirrors the multi-robot structure of the task, and admits distributed training via Gaussian belief propagation (GBP). Our loopy model outperforms Tree-Structured GPs \cite{bui2014tree} and can be trained online and in settings with dynamic connectivity. We show that such distributed, asynchronous training can reach the same performance as a centralised, batch-trained model, albeit with slower convergence. Last, we compare to DiNNO \cite{yu2022dinno}, a distributed neural network (NN) optimiser, and find DistGP achieves superior accuracy, is more robust to sparse communication and is better able to learn continually.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
