Abstract
The peculiarities of correlation-extreme visual navigation are considered. Descriptors with 64 elements of feature points of surface images are selected on the basis of the speed-up robust feature method. An analysis of possible criteria correlation functions is carried out to find the best match between the template descriptors and current images. The use of normalized correlation function is proposed based on the matrix multiplication properties of descriptors. It allows minimizing the number of false matches in comparison with the Euclidean distance in the descriptor space. The proposed matching strategy sufficiently decreases the computation time.
Highlights
Miniature unmanned aircraft systems (UAS) are increasingly used for various purposes like monitoring of ground or water surface, surveillance, reconnaissance and others
For a small UAS with a lowcost inertial measurement unit (IMU) based on economic micro-electromechanical sensors (MEMS), the drift of errors in a few seconds can lead to unreliable positioning
The normilized cross-correlation function is highly recommended for template matching (Briechle, Hanebeck 2001); 2) difference criteria functions, represented as follows: K3= (l) M[| F − f |p], (6)
Summary
Miniature unmanned aircraft systems (UAS) are increasingly used for various purposes like monitoring of ground or water surface, surveillance, reconnaissance and others. The functioning of UAS in urban areas is significantly limited by severe requirements for the accuracy of navigation and control and collision avoidance solutions. The basic navigation complex of UAS includes the integrated inertial navigation system (INS) and satellite navigation system (SNS). In the case of satellite signal loss the navigation solution is obtained from the inertial measurement unit (IMU), which, has its own errors that grow over time. The operation of SNS in an urban area is limited by significant multipath errors due to multiple signal reflections from obstacles, buildings and so on. Over the last decade promising results of visual UAS navigation (Conte, Doherty 2011) have been achieved by the rapid development of computing power that makes available the use of heavy computation of image processing methods in real-time mode. The modified speed-up robust feature (SURF) method (Bay et al 2006) will be considered for the practical implementation aboard the UAS
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