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

Abstract— 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.

Model construction for a sequence of measurements

Local experts indicated by differently colored dots.

Areas of responsibility of local experts.

Estimation of the signed distance field and model pseudo-inputs.

Local experts and signed distance field predicted in their area of responsibility.

Predicted signed distance field.