Zhenlu Jin

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Scene matching is used in the vision based automated navigation error correction technique in the absence of global positioning systems for unmanned aerial vehicles. When knowledge of landmarks in the scene is known a priori, the scene matching can be carried out in a more accurate and efficient way by considering a landmarks-only matching process. In this(More)
Vision based navigation error correction may serve as a backup technique to support autonomous navigation of the unmanned aerial vehicle (UAV) in the absence of the conventional navigation system. An efficient and accurate scene matching method is desirable for the implementation of such a system to improve the flight control capabilities of UAVs. In this(More)
Matching-area suitability analysis in vision navigation system for unmanned aerial vehicle (UAV) is a very worthy but full of challenges research area. In this paper, a multi-feature fusion based visual saliency model (MFF-VSM) was established by introducing invariant features of speeded-up robust features (SURF) directly into the visual saliency model,(More)
Optimal determination of a UAV using a vision-based system to match images against a database is an important problem. It can be reformulated to the problem of using multiregion scene registration to match areas of a noisy and distorted image to a geo-referenced image. Under the assumptions that the mapping between sensed and geo-referenced images preserves(More)
In the process of building vision based navigation systems, UAV self-localisation by image registration against a database is a challenging issue. The issue arises from the requirement to register a scene from an aerial image with a geo-referenced image. This is typically done by matching features across multiple regions in the aerial image in the presence(More)
A scene matching based visual SLAM (simultaneous localization and mapping) navigation algorithm is proposed for SUAV (small unmanned aerial vehicle) which described by EKF (Extended Kalman Filtering). Firstly, a scene matching method with weighted Hausdorff distance was introduced for waypoints accurate abstraction. On this foundation, the SUAV's nonlinear(More)
Consider the hybrid systems with nonlinear property, and the sensor measurements with unknown and time-varying systematic error in this paper. In order to obtain the joint least square (LS) estimation of state and systematic error, a new method - JE-EM (joint estimation-expectation maximization) is proposed. In this paper, the relationship between the(More)
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