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. We focus on probabilistic variants of mapping due to its potential utility in downstream tasks such as uncertainty-aware path-planning. 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 put forth an Incremental Sparse Gaussian Process (GP) methodology for multi-robot mapping, where the regression is over a truncated signed-distance field (TSDF). Doing so permits each robot in the network to track a local estimate of a pseudo-point approximation GP posterior and perform weighted averaging of its parameters with those of its (possibly time-varying) set of neighbors. We establish conditions on the pseudo-point representation, as well as communication 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.
翻译:多试剂绘图是复杂环境中自主机器人任务协调和执行的根本重要能力。虽然已提出使用单个平台进行绘图的成功算法,但机器人团队合作在线制图仍是一个很大的挑战。我们注重绘图的概率变体,因为它在不确定性-认知路径规划等下游任务中的潜在效用。使这一能力得以实现的一个关键问题是,如何在单个平台之间处理和汇总逐渐观察到的当地信息,特别是当这些平台的通信能力处于间歇状态时。我们提出了多机器人绘图的递增式Sparse Gossian(GP)程序(GP)方法(GP),该方法的回归已超过一个短短的签名距离域(TSDF ) 。我们这样做可以让网络中的每个机器人跟踪一个假点近似 GP 远地点的本地估计数,并用其参数与其(可能时间变化的)一组邻居的参数进行加权平均。我们建立了假点代表条件以及通信协议,例如机器人的本地GPS(GP)与全球汇总信息相交汇。我们提供了进一步实验,以证实我们的理论结论性多式制图。