  Heterogeneous multi-robot sensing systems are able to characterize physical
processes more comprehensively than homogeneous systems. Access to multiple
modalities of sensory data allow such systems to fuse information between
complementary sources and learn richer representations of a phenomenon of
interest. Often, these data are correlated but vary in fidelity, i.e., accuracy
(bias) and precision (noise). Low-fidelity data may be more plentiful, while
high-fidelity data may be more trustworthy. In this paper, we address the
problem of multi-robot online estimation and coverage control by combining low-
and high-fidelity data to learn and cover a sensory function of interest. We
propose two algorithms for this task of heterogeneous learning and coverage --
namely Stochastic Sequencing of Multi-fidelity Learning and Coverage (SMLC) and
Deterministic Sequencing of Multi-fidelity Learning and Coverage (DMLC) -- and
prove that they converge asymptotically. In addition, we demonstrate the
empirical efficacy of SMLC and DMLC through numerical simulations.
