  Autonomous navigation in unknown environments is challenging and demands the
consideration of both geometric and semantic information in order to parse the
navigability of the environment. In this work, we propose a novel space
modeling framework, Visual-Geometry Sparse Gaussian Process (VG-SGP), that
simultaneously considers semantics and geometry of the scene. Our proposed
approach can overcome the limitation of visual planners that fail to recognize
geometry associated with the semantic and the geometric planners that
completely overlook the semantic information which is very critical in
real-world navigation. The proposed method leverages dual Sparse Gaussian
Processes in an integrated manner; the first is trained to forecast
geometrically navigable spaces while the second predicts the semantically
navigable areas. This integrated model is able to pinpoint the overlapping
(geometric and semantic) navigable space. The simulation and real-world
experiments demonstrate that the ability of the proposed VG-SGP model, coupled
with our innovative navigation strategy, outperforms models solely reliant on
visual or geometric navigation algorithms, highlighting a superior adaptive
behavior.
