Visual Place Recognition for Aerial Robotics: Exploring Accuracy-Computation Trade-off for Local Image Descriptors

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 me...

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Bibliographic Details
Main Authors: Ferrarini, Bruno, Waheed, Maria, Waheed, Sania, Ehsan, Shoaib, Milford, Michael, McDonald-Maier, Klaus D.
Format: Conference Proceeding
Language:English
Subjects:
Comparison
Dictionaries
Feature extraction
Hardware
Local Image Descriptors
Task analysis
Training
Unmanned aerial vehicles
Visual Place Recognition
Visualization
Online Access:Request full text
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Summary: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.
ISSN:2471-769X
DOI:10.1109/AHS.2019.00011