  Non-geometric mobility hazards such as rover slippage and sinkage posing
great challenges to costly planetary missions are closely related to the
mechanical properties of terrain. In-situ proprioceptive processes for rovers
to estimate terrain mechanical properties need to experience different slip as
well as sinkage and are helpless to untraversed regions. This paper proposes to
predict terrain mechanical properties with vision in the distance, which
expands the sensing range to the whole view and can partly halt potential
slippage and sinkage hazards in the planning stage. A semantic-based method is
designed to predict bearing and shearing properties of terrain in two stages
connected with semantic clues. The former segmentation phase segments terrain
with a light-weighted network promising to be applied onboard with competitive
93% accuracy and high recall rate over 96%, while the latter inference phase
predicts terrain properties in a quantitative manner based on human-like
inference principles. The prediction results in several test routes are 12.5%
and 10.8% in full-scale error and help to plan appropriate strategies to avoid
suffering non-geometric hazards.
