Ensemble of Sparse Gaussian Process Experts for Implicit Surface Mapping with Streaming Data
This work addresses the challenge of efficient map creation for robotics planning and navigation, but it is incremental as it builds on existing Gaussian process and ensemble methods.
The paper tackles the problem of learning compact, continuous implicit surface maps from streaming range data in robotics by using an ensemble of sparse Gaussian process experts that trade off model complexity and prediction error. The results show that the method achieves performance comparable to or better than exact GP regression with subsampled data on synthetic and real-world datasets.
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.