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-ofthe-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.
Recent advances in image description and matching allowed significant improvements in Visual Place Recognition (VPR). The wide variety of methods proposed so far and the increase of the interest in the field have rendered the problem of evaluating VPR methods an important task. As part of the localization process, VPR is a critical stage for many robotic applications and it is expected to perform reliably in any location of the operating environment. To design more reliable and effective localization systems this letter presents a generic evaluation framework based on the new Extended Precision performance metric for VPR. The proposed framework allows assessment of the upper and lower bounds of VPR performance and finds statistically significant performance differences between VPR methods. The proposed evaluation method is used to assess several state-of-the-art techniques with a variety of imaging conditions that an autonomous navigation system commonly encounters on long term runs. The results provide new insights into the behaviour of different VPR methods under varying conditions and help to decide which technique is more appropriate to the nature of the venture or the task assigned to an autonomous robot.
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