  Autonomous mobile robots (AMRs) equipped with high-quality cameras have
revolutionized the field of inspections by providing efficient and
cost-effective means of conducting surveys. The use of autonomous inspection is
becoming more widespread in a variety of contexts, yet it is still challenging
to acquire the best inspection information autonomously. In situations where
objects may block a robot's view, it is necessary to use reasoning to determine
the optimal points for collecting data. Although researchers have explored
cloud-based applications to store inspection data, these applications may not
operate optimally under network constraints, and parsing these datasets can be
manually intensive. Instead, there is an emerging requirement for AMRs to
autonomously capture the most informative views efficiently. To address this
challenge, we present an autonomous Next-Best-View (NBV) framework that
maximizes the inspection information while reducing the number of pictures
needed during operations. The framework consists of a formalized evaluation
metric using ray-tracing and Gaussian process interpolation to estimate
information reward based on the current understanding of the partially-known
environment. A derivative-free optimization (DFO) method is used to sample
candidate views in the environment and identify the NBV point. The proposed
approach's effectiveness is shown by comparing it with existing methods and
further validated through simulations and experiments with various vehicles.
