  Most existing motion planning algorithms assume that a map (of some quality)
is fully determined prior to generating a motion plan. In many emerging
applications of robotics, e.g., fast-moving agile aerial robots with
constrained embedded computational platforms and visual sensors, dense maps of
the world are not immediately available, and they are computationally expensive
to construct. We propose a new algorithm for generating plan graphs which
couples the perception and motion planning processes for computational
efficiency. In a nutshell, the proposed algorithm iteratively switches between
the planning sub-problem and the mapping sub-problem, each updating based on
the other until a valid trajectory is found. The resulting trajectory retains a
provable property of providing an optimal trajectory with respect to the full
(unmapped) environment, while utilizing only a fraction of the sensing data in
computational experiments.
