Soil assessment is important for mobile robot planning and navigation on natural and planetary environments. Terramechanic characteristics can be inferred from the thermal behaviour of soils under the influence of sunlight using remote sensors such as Long-Wave Infrared cameras. However, this behaviour is greatly affected by the low atmospheric pressures of planets such as Mars, so practical models are needed to relate robot remote sensing data on Earth to target planetary exploration conditions. This article proposes a general framework based on multipurpose environmental chambers to generate representative diurnal cycle dataset pairs that can be useful to relate the thermal behaviour of a soil on Earth to the corresponding behaviour under planetary pressure conditions using remote sensing. Furthermore, we present an application of the proposed framework to generate datasets using the UMA-Laserlab chamber, which can replicate the atmospheric \ch{CO2} composition of Mars. In particular, we analyze the thermal behaviour of four soil samples of different granularity by comparing replicated Martian surface conditions and their Earth's diurnal cycle equivalent. Results indicate a correlation between granularity and thermal inertia that is consistent with available Mars surface measurements recorded by rovers. The resulting dataset pairs, consisting of representative diurnal cycle thermal images with heater, air, and subsurface temperatures, have been made available for the scientific community.

翻译：土壤评估对于移动机器人在自然与行星环境中的规划及导航至关重要。基于远程传感器（如长波红外相机），可通过阳光影响下土壤的热行为推断其地形力学特性。然而，火星等行星的低气压条件会显著改变这一行为，因此需要建立实用模型来关联地球上的机器人遥感数据与目标行星的勘探条件。本文提出一种基于多功能环境舱的通用框架，通过生成具有代表性的昼夜循环数据集对，利用遥感技术关联地球土壤热行为与对应行星气压条件下的表现。此外，我们展示了该框架在UMA-Laserlab环境舱中的应用——该舱体可模拟火星大气中的\ch{CO2}成分。具体而言，通过对比四份不同颗粒度土壤样本在模拟火星表面条件与地球等效昼夜循环下的热行为，发现颗粒度与热惯性之间的关联性与现有火星车实地测量数据一致。最终生成的数据集对（包含具有代表性的昼夜循环热图像、加热器温度、空气温度及地下温度）已向科学界开放。