Dense Incremental Metric-Semantic Mapping via Sparse Gaussian Process Regression

Abstract

We develop an online probabilistic metric-semantic mapping approach for autonomous robots relying on streaming RGB-D observations. We cast this problem as a Bayesian inference task, requiring encoding both the geometric surfaces and semantic labels (e.g., chair, table, wall) of the unknown environment. We propose an online Gaussian Process (GP) training and inference approach, which avoids the complexity of GP classification by regressing a truncated signed distance function representation of the regions occupied by different semantic classes. Online regression is enabled through sparse GP approximation, compressing the training data to a finite set of inducing points, and through spatial domain partitioning into an Octree data structure with overlapping leaves. Our experiments demonstrate the effectiveness of this technique for large-scale probabilistic metric-semantic mapping of 3D environments. A distinguishing feature of our approach is that the generated maps contain full continuous distributional information about the geometric surfaces and semantic labels, making them appropriate for uncertainty-aware planning.