  Traditional approaches for active mapping focus on building geometric maps.
For most real-world applications, however, actionable information is related to
semantically meaningful objects in the environment. We propose an approach to
the active metric-semantic mapping problem that enables multiple heterogeneous
robots to collaboratively build a map of the environment. The robots actively
explore to minimize the uncertainties in both semantic (object classification)
and geometric (object modeling) information. We represent the environment using
informative but sparse object models, each consisting of a basic shape and a
semantic class label, and characterize uncertainties empirically using a large
amount of real-world data. Given a prior map, we use this model to select
actions for each robot to minimize uncertainties. The performance of our
algorithm is demonstrated through multi-robot experiments in diverse real-world
environments. The proposed framework is applicable to a wide range of
real-world problems, such as precision agriculture, infrastructure inspection,
and asset mapping in factories. A demo video can be found at
https://youtu.be/S86SgXi54oU.
