No abstract

No abstract

The UAV industry is growing rapidly in an attempt to serve both military and commercial applications. A crucial aspect in the development of UAVs is the reduction of navigational sensor costs while maintaining accurate navigation. Advances in visual sensor solutions with traditional navigation sensors are proving to be significantly promising in replacing traditional IMU or GPS systems for many mission scenarios. The basic concept behind Vision Based Navigation (VBN) is to find the matches between a set of features in real-time captured images taken by the imaging sensor on the UAV and database images. A scale and rotation invariant image matching algorithm is a key element for VBN of aerial vehicles. Matches between the geo-referenced database images and the new real-time captured ones are determined by employing the fast Speeded Up Robust Features (SURF) algorithm. The SURF algorithm consists mainly of two steps: the first is the detection of points of interest and the second is the creation of descriptors for each of these points. In this research paper, two major factors are investigated and tested to efficiently create the descriptors for each point of interest. The first factor is the dimension of the descriptor for a given point of interest. The dimension is affected by the number of descriptor sub-regions which consequently affects the matching time and the accuracy. SURF performance has been investigated and tested using different dimensions of the descriptor.The second factor is the number of sample points in each sub-region which are used to build the descriptor of the point of interest. SURF performance has been investigated and tested by changing the number of sample points in each sub-region where the matching accuracy is affected.Assessments of the SURF performance and consequently on UAV VBN are investigated.

No abstract

No abstract