  In this work, we address the estimation, planning, control and mapping
problems to allow a small quadrotor to autonomously inspect the interior of
hazardous damaged nuclear sites. These algorithms run onboard on a
computationally limited CPU. We investigate the effect of varying illumination
on the system performance. To the best of our knowledge, this is the first
fully autonomous system of this size and scale applied to inspect the interior
of a full scale mock-up of a Primary Containment Vessel (PCV). The proposed
solution opens up new ways to inspect nuclear reactors and to support nuclear
decommissioning, which is well known to be a dangerous, long and tedious
process. Experimental results with varying illumination conditions show the
ability to navigate a full scale mock-up PCV pedestal and create a map of the
environment, while concurrently avoiding obstacles.
