Visual Place Recognition for Aerial Robotics: Exploring Accuracy-Computation Trade-off for Local Image Descriptors
This addresses the challenge of enabling robust VPR for UAVs with limited computational power, though it is incremental as it focuses on evaluating existing descriptors rather than proposing new methods.
The paper tackled the problem of Visual Place Recognition (VPR) for small Unmanned Aerial Vehicles (UAVs) by evaluating local image descriptors to balance accuracy and computational efficiency, finding that a trade-off between these factors is inevitable on resource-constrained hardware.
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.