Distributed Gaussian Process Mapping for Robot Teams with Time-varying Communication
Distributed Gaussian Process Mapping for Robot Teams with Time-varying Communication
复制标题
DOI：
10.23919/acc53348.2022.9867415
复制
发表时间：
2021-10
期刊：
影响因子：
--
通讯作者：
James Di;Ehsan Zobeidi;Alec Koppel;Nikolay A. Atanasov
中科院分区：
文献类型：
--
作者：
James Di;Ehsan Zobeidi;Alec Koppel;Nikolay A. Atanasov
多智能体映射是复杂环境下自主机器人任务协调和执行的一种重要能力。虽然成功的算法已经提出了使用个人平台的映射，合作的机器人团队的在线映射仍然是一个很大的挑战。实现这种能力的一个关键问题是如何处理和聚合各个平台之间增量观察到的本地信息，特别是当它们的通信能力是间歇性的时。我们采用截断符号距离场（TSDF）的地图表示，并提出了一个增量稀疏高斯过程（GP）的方法来回归TSDF多机器人映射。这样做允许网络中的每个机器人跟踪近似GP后验的局部估计，并对其参数与其（可能随时间变化的）邻居集进行加权平均。我们专注于概率变量的映射，由于其潜在的效用，如不确定性感知路径规划的下游任务。我们建立条件的GP表示，以及通信协议，使机器人的本地GP收敛到一个全球聚合的信息。我们进一步提供实验，证实我们的理论研究结果的概率多机器人映射。
Multi-agent mapping is a fundamentally important capability for autonomous robot task coordination and execution in complex environments. While successful algorithms have been proposed for mapping using individual platforms, cooperative online mapping for teams of robots remains largely a challenge. A critical question to enabling this capability is how to process and aggregate incrementally observed local information among individual platforms, especially when their ability to communicate is intermittent. We employ truncated signed-distance field (TSDF) as the map representation, and propose an Incremental Sparse Gaussian Process (GP) methodology to regress over TSDF for multi-robot mapping. Doing so permits each robot in the network to track a local estimate of an approximated GP posterior and perform weighted averaging of its parameters with its (possibly time-varying) set of neighbors. We focus on probabilistic variants of mapping due to its potential utility in down-stream tasks such as uncertainty-aware path-planning. We establish conditions on the GP representation, as well as communications protocol, such that robots’ local GPs converge to the one with globally aggregated information. We further provide experiments that corroborate our theoretical findings for probabilistic multi-robot mapping.