Distributed Gaussian Process Mapping for Robot Teams with Time-varying Communication
This work addresses the challenge of probabilistic multi-robot mapping for autonomous coordination in complex environments, representing an incremental advancement in handling time-varying communication.
The paper tackles the problem of cooperative online mapping for robot teams with intermittent communication by proposing an Incremental Sparse Gaussian Process method over a truncated signed-distance field, enabling local GP estimates to converge to a global one through weighted averaging of parameters among neighbors.
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.