  In many autonomous mapping tasks, the maps cannot be accurately constructed
due to various reasons such as sparse, noisy, and partial sensor measurements.
We propose a novel map prediction method built upon the recent success of
Low-Rank Matrix Completion. The proposed map prediction is able to achieve both
map interpolation and extrapolation on raw poor-quality maps with missing or
noisy observations. We validate with extensive simulated experiments that the
approach can achieve real-time computation for large maps, and the performance
is superior to the state-of-the-art map prediction approach - Bayesian Hilbert
Mapping in terms of mapping accuracy and computation time. Then we demonstrate
that with the proposed real-time map prediction framework, the coverage
convergence rate (per action step) for a set of representative coverage
planning methods commonly used for environmental modeling and monitoring tasks
can be significantly improved.
