  Robots are often required to operate in environments where humans are not
present, but yet require the human context information for better human-robot
interaction. Even when humans are present in the environment, detecting their
presence in cluttered environments could be challenging. As a solution to this
problem, this paper presents the concept of spatial affordance map which learns
human context by looking at geometric features of the environment. Instead of
observing real humans to learn human context, it uses virtual human models and
their relationships with the environment to map hidden human affordances in 3D
scenes by placing virtual skeleton models in 3D scenes with their confidence
values. The spatial affordance map learning problem is formulated as a
multi-label classification problem that can be learned using Support Vector
Machine (SVM) based learners. Experiments carried out in a real 3D scene
dataset recorded promising results and proved the applicability of
affordance-map for mapping human context.
