  Rendezvous aims at gathering all robots at a specific location, which is an
important collaborative behavior for multirobot systems. However, in an unknown
environment, it is challenging to achieve rendezvous. Previous researches
mainly focus on special scenarios where communication is not allowed and each
robot executes a random searching strategy, which is highly time-consuming,
especially in large-scale environments. In this work, we focus on rendezvous in
unknown environments where communication is available. We divide this task into
two steps: rendezvous based environment exploration with relative pose (RP)
estimation and rendezvous point election. A new strategy called partitioned and
incomplete exploration for rendezvous (PIER) is proposed to efficiently explore
the unknown environment, where lightweight topological maps are constructed and
shared among robots for RP estimation with very few communications. Then, a
rendezvous point selection algorithm based on the merged topological map is
proposed for efficient rendezvous for multi-robot systems. The effectiveness of
the proposed methods is validated in both simulations and real-world
experiments.
