  Creating maps is an essential task in robotics and provides the basis for
effective planning and navigation. In this paper, we learn a compact and
continuous implicit surface map of an environment from a stream of range data
with known poses. For this, we create and incrementally adjust an ensemble of
approximate Gaussian process (GP) experts which are each responsible for a
different part of the map. Instead of inserting all arriving data into the GP
models, we greedily trade-off between model complexity and prediction error.
Our algorithm therefore uses less resources on areas with few geometric
features and more where the environment is rich in variety. We evaluate our
approach on synthetic and real-world data sets and analyze sensitivity to
parameters and measurement noise. The results show that we can learn compact
and accurate implicit surface models under different conditions, with a
performance comparable to or better than that of exact GP regression with
subsampled data.
