Mobile robots used for information gathering tasks rely on dense, predictive mapping of large-scale regions to determine where to take measurements. Current approaches to mapping commonly rely on Gaussian process regression to spatially correlate data, extrapolate from sparse samples, and estimate uncertainty. However, these approaches do not incorporate meaningful information about physical processes that contribute to the scientific observations being recorded, instead focusing on only predicting measurements or relying on black box expert-based systems to incorporate physically meaningful information.

In this work, we introduce physical process-informed mapping, a framework for integrating scientific knowledge about physical processes into mapping for mobile exploration robots. This approach provides two advantages for exploration. First, the hierarchical statistical model underlying the mapping framework is capable of providing predictions and uncertainty estimates for both the sensor measurements and the latent natural phenomena driving these measurements in a dense, probabilistic manner. Second, various components of the mapping approach provide methods to measure and adjust the importance of models of scientific knowledge, enabling learning about the conditions under which these models are more or less relevant to the latent phenomenon. Taken together, these methods improve the interpretability of mapping for autonomous scientific discovery, enabling future robotic explorers to take advantage of a more in-depth representation of information, and can provide increases in the predictive accuracy of mapping.

David Wettergreen (Chair)

Wennie Tabib

Mikael Kuusela

Terrence Fong (NASA Ames Research Center)