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

Ensemble of Sparse Gaussian Process Experts for Implicit Surface Mapping with Streaming Data