  Visual navigation has been widely used for state estimation of micro aerial
vehicles (MAVs). For stable visual navigation, MAVs should generate
perception-aware paths which guarantee enough visible landmarks. Many previous
works on perception-aware path planning focused on sampling-based planners.
However, they may suffer from sample inefficiency, which leads to computational
burden for finding a global optimal path. To address this issue, we suggest a
perception-aware path planner which utilizes topological information of
environments. Since the topological class of a path and visible landmarks
during traveling the path are closely related, the proposed algorithm checks
distinctive topological classes to choose the class with abundant visual
information. Topological graph is extracted from the generalized Voronoi
diagram of the environment and initial paths with different topological classes
are found. To evaluate the perception quality of the classes, we divide the
initial path into discrete segments where the points in each segment share
similar visual information. The optimal class with high perception quality is
selected, and a graph-based planner is utilized to generate path within the
class. With simulations and real-world experiments, we confirmed that the
proposed method could guarantee accurate visual navigation compared with the
perception-agnostic method while showing improved computational efficiency than
the sampling-based perception-aware planner.
