  Multi-agent exploration of a bounded 3D environment with unknown initial
positions of agents is a challenging problem. It requires quickly exploring the
environments as well as robustly merging the sub-maps built by the agents. We
take the view that the existing approaches are either aggressive or
conservative: Aggressive strategies merge two sub-maps built by different
agents together when overlap is detected, which can lead to incorrect merging
due to the false-positive detection of the overlap and is thus not robust.
Conservative strategies direct one agent to revisit an excessive amount of the
historical trajectory of another agent for verification before merging, which
can lower the exploration efficiency due to the repeated exploration of the
same space. To intelligently balance the robustness of sub-map merging and
exploration efficiency, we develop a new approach for lidar-based multi-agent
exploration, which can direct one agent to repeat another agent's trajectory in
an \emph{adaptive} manner based on the quality indicator of the sub-map merging
process. Additionally, our approach extends the recent single-agent
hierarchical exploration strategy to multiple agents in a \emph{cooperative}
manner by planning for agents with merged sub-maps together to further improve
exploration efficiency. Our experiments show that our approach is up to 50\%
more efficient than the baselines on average while merging sub-maps robustly.
