Slope-aware Maximum Uncertainty Sampling for Time-efficient Robotic Mapping on Rough Terrain

Robotic information gathering methods combining Gaussian Process (GP) regression and Bayesian Optimization (BO) are effective for mapping of soil mechanical properties in uncertain or unknown extraterrestrial terrain. However, standard applications of such methods often model robot traversability as a simple go/no-go constraint, without fully incorporating it as a variable cost that impacts overall efficiency. This limitation is critical on rough terrains like the lunar surface, where traversability heavily influences the trade-off between survey time and modeling accuracy. To address this gap, we propose a comprehensive approach for slope-aware robotic information gathering. The core of this approach is a novel adaptive sampling method, Slope-aware Maximum Uncertainty Sampling (SaMUS), which features a new BO acquisition function that explicitly incorporates terrain slope to balance uncertainty reduction with path traversability. We demonstrate the effectiveness of our method through an extensive simulation study in two environment types: controlled basic terrains for qualitative analysis and realistic lunar terrains for evaluating the performance of the proposed method. The results show that the proposed approach generates significantly more time-efficient survey paths by strategically avoiding highslope regions. Quantitative analysis on the realistic terrains further demonstrates that our method achieves a better tradeoff between survey time and modeling accuracy, particularly on flat and steep terrains. Finally, we provide practical guidelines for selecting optimal parameters based on terrain class and operational time constraints.