  Autonomous hazard detection and avoidance is a key technology for future
landing missions in unknown surface conditions. Current state-of-the-art
stochastic algorithms assume simple Gaussian measurement noise on dense,
high-fidelity digital elevation maps, limiting the algorithm's applicability.
This paper introduces a new stochastic hazard detection algorithm capable of
more general topographic uncertainty by leveraging the Gaussian random field
regression. The proposed approach enables the safety assessment with imperfect
and sparse sensor measurements, which allows hazard detection operations under
more diverse conditions. We demonstrate the performance of the proposed
approach on the existing Mars digital terrain models.
