  We present a solution to multi-robot distributed semantic mapping of novel
and unfamiliar environments. Most state-of-the-art semantic mapping systems are
based on supervised learning algorithms that cannot classify novel observations
online. While unsupervised learning algorithms can invent labels for novel
observations, approaches to detect when multiple robots have independently
developed their own labels for the same new class are prone to erroneous or
inconsistent matches. These issues worsen as the number of robots in the system
increases and prevent fusing the local maps produced by each robot into a
consistent global map, which is crucial for cooperative planning and joint
mission summarization. Our proposed solution overcomes these obstacles by
having each robot learn an unsupervised semantic scene model online and use a
multiway matching algorithm to identify consistent sets of matches between
learned semantic labels belonging to different robots. Compared to the state of
the art, the proposed solution produces 20-60% higher quality global maps that
do not degrade even as many more local maps are fused.
