  Visual Place Recognition (VPR) is a fundamental yet challenging task for
small Unmanned Aerial Vehicle (UAV). The core reasons are the extreme viewpoint
changes, and limited computational power onboard a UAV which restricts the
applicability of robust but computation intensive state-of-the-art VPR methods.
In this context, a viable approach is to use local image descriptors for
performing VPR as these can be computed relatively efficiently without the need
of any special hardware, such as a GPU. However, the choice of a local feature
descriptor is not trivial and calls for a detailed investigation as there is a
trade-off between VPR accuracy and the required computational effort. To fill
this research gap, this paper examines the performance of several
state-of-the-art local feature descriptors, both from accuracy and
computational perspectives, specifically for VPR application utilizing standard
aerial datasets. The presented results confirm that a trade-off between
accuracy and computational effort is inevitable while executing VPR on
resource-constrained hardware.
