Feature point matching is an important step for many vision-based unmanned-aerial-vehicle applications. This paper presents the development of a new feature descriptor for feature point matching that is well suited for micro unmanned aerial vehicles equipped with a low-resource, compact, lightweight, low-power embedded vision sensor. The Basis Sparse-Coding Inspired Similarity descriptor uses theory taken from sparse coding to provide an efficient image feature description method for frame-to-frame feature point matching. This descriptor requires simple mathematical operations, uses comparatively small memory storage, and can support color and grayscale feature descriptions. It is an excellent candidate for implementation on low-resource systems that require real-time performance, where complex mathematical operations are prohibitively expensive. To demonstrate its performance, the feature matching result was used to calculate a frame-to-frame homography that is essential to unmanned-aerial-vehicle applications such as pose estimation and obstacle detection for navigation. The proposed descriptor was tested on two video sequences and one dataset of real aerial images. Experimental results show that, along with performing in situations where existing complex descriptors cannot be used, the Basis Sparse-Coding Inspired Similarity descriptor also performs slightly better than these other methods on the task of homography calculation. Our experimental results and analysis show that the Basis Sparse-Coding Inspired Similarity descriptor is an excellent candidate for a resource-limited vision sensor for micro unmanned aerial vehicles.
Novel Feature Descriptor for Low-Resource Embedded Vision Sensors for Micro Unmanned-Aerial-Vehicle Applications
Journal of Aerospace Information Systems ; 10 , 8 ; 385-395
2013-08-12
11 pages
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
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