Title: History-aware Autonomous Exploration in Confined Environments using MAVs,
Abstract: Many scenarios require a robot to be able to explore its 3D environment
online without human supervision. This is especially relevant for inspection
tasks and search and rescue missions. To solve this high-dimensional path
planning problem, sampling-based exploration algorithms have proven successful.
However, these do not necessarily scale well to larger environments or spaces
with narrow openings. This paper presents a 3D exploration planner based on the
principles of Next-Best Views (NBVs). In this approach, a Micro-Aerial Vehicle
(MAV) equipped with a limited field-of-view depth sensor randomly samples its
configuration space to find promising future viewpoints. In order to obtain
high sampling efficiency, our planner maintains and uses a history of visited
places, and locally optimizes the robot's orientation with respect to
unobserved space. We evaluate our method in several simulated scenarios, and
compare it against a state-of-the-art exploration algorithm. The experiments
show substantial improvements in exploration time ($2\times$ faster),
computation time, and path length, and advantages in handling difficult
situations such as escaping dead-ends (up to $20\times$ faster). Finally, we
validate the on-line capability of our algorithm on a computational constrained
real world MAV.