  Robot planning in partially observable domains is difficult, because a robot
needs to estimate the current state and plan actions at the same time. When the
domain includes many objects, reasoning about the objects and their
relationships makes robot planning even more difficult. In this paper, we
develop an algorithm called scene analysis for robot planning (SARP) that
enables robots to reason with visual contextual information toward achieving
long-term goals under uncertainty. SARP constructs scene graphs, a factored
representation of objects and their relations, using images captured from
different positions, and reasons with them to enable context-aware robot
planning under partial observability. Experiments have been conducted using
multiple 3D environments in simulation, and a dataset collected by a real
robot. In comparison to standard robot planning and scene analysis methods, in
a target search domain, SARP improves both efficiency and accuracy in task
completion. Supplementary material can be found at https://tinyurl.com/sarp22
