Abstract
Aiming at enhancing the accuracy and reliability of velocity calculation in vision navigation, an improved method is proposed in this paper. The method integrates Mask-R-CNN (Mask Region-based Convolutional Neural Network) and K-Means with the pyramid Lucas Kanade algorithm in order to reduce the harmful effect of moving objects on velocity calculation. Firstly, Mask-R-CNN is used to recognize the objects which have motions relative to the ground and covers them with masks to enhance the similarity between pixels and to reduce the impacts of the noisy moving pixels. Then, the pyramid Lucas Kanade algorithm is used to calculate the optical flow value. Finally, the value is clustered by the K-Means algorithm to abandon the outliers, and vehicle velocity is calculated by the processed optical flow. The prominent advantages of the proposed algorithm are (i) decreasing the bad impacts to velocity calculation, due to the objects which have relative motions; (ii) obtaining the correct optical flow sets and velocity calculation outputs with less fluctuation; and (iii) the applicability enhancement of the optical flow algorithm in complex navigation environment. The proposed algorithm is tested by actual experiments. Results with superior precision and reliability show the feasibility and effectiveness of the proposed method for vehicle velocity calculation in vision navigation system.
Highlights
The unmanned combat platform is a developing war mode in future wars, where a micro-unmanned aerial vehicle (UAV) occupies a significant position
Motions captured by the UAV can be divided into two parts; global motions caused by the changes of background and local motions caused by moving foreground in the scene. As these motions have different speeds, the local motion may have serious impact on optical flow values. Considering these complex environments in optical flow calculation, this paper proposes an improved pyramid Lucas Kanade (LK) optical flow algorithm by combining Mask-R-CNN (Mask Region-based Convolutional Neural Network) [28] and K-Means [29]
GPSrecognition, the addition of similar color masks, as well as the final velocity calculation based on optical flow, three kinds of typicalSensor application circumstances are considered in our experiments, which are explained in detail as category
Summary
The unmanned combat platform is a developing war mode in future wars, where a micro-unmanned aerial vehicle (UAV) occupies a significant position. This method can improve the accuracy of the algorithm These algorithms are not adaptable enough to accurately calculate the velocity of aircraft in complex environments, where people and other objects like animals and cars, which have motions relative to the ground, may lead to extra pixel movement. Environment noises resulting from interference of moving objects may result in wrong optical flow values and reduce the accuracy of vehicle velocity calculation. As these motions have different speeds, the local motion may have serious impact on optical flow values Considering these complex environments in optical flow calculation, this paper proposes an improved pyramid LK optical flow algorithm by combining Mask-R-CNN (Mask Region-based Convolutional Neural Network) [28] and K-Means [29].
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