Due to the trajectory error of the low-precision position and orientation system (POS) used in unmanned aerial laser scanning (ULS), discrepancies usually exist between adjacent LiDAR (Light Detection and Ranging) strips. Strip adjustment is an effective way to eliminate these discrepancies. However, it is difficult to apply existing strip adjustment methods in mountainous areas with few artificial objects. Thus, digital elevation model-iterative closest point (DEM-ICP), a pair-wise registration method that takes topography features into account, is proposed in this paper. First, DEM-ICP filters the point clouds to remove the non-ground points. Second, the ground points are interpolated to generate continuous DEMs. Finally, a point-to-plane ICP algorithm is performed to register the adjacent DEMs with the overlapping area. A graph-based optimization is utilized following DEM-ICP to estimate the correction parameters and achieve global consistency between all strips. Experiments were carried out using eight strips collected by ULS in mountainous areas to evaluate the proposed method. The average root-mean-square error (RMSE) of all data was less than 0.4 m after the proposed strip adjustment, which was only 0.015 m higher than the result of manual registration (ground truth). In addition, the plane fitting accuracy of lateral point clouds was improved 4.2-fold, from 1.565 to 0.375 m, demonstrating the robustness and accuracy of the proposed method.
Traditional defensive techniques are usually static and passive, and appear weak to confront highly adaptive and stealthy attacks. As a novel security theory, Cyberspace Mimic Defense (CMD) creates asymmetric uncertainty that favors the defender. CMD constructs multiple executors which are diverse functional equivalent variants for the protected target and arbitral mechanism. In this way, CMD senses the results of current running executors and changes the attack surface. Although CMD enhances the security of systems, there are still some critical gaps with respect to design a defensive strategy under costs and security. In this paper, we propose a dual model to dynamically select the number of executors being reconfigured according to the states of the executors. First, we establish a Markov anti-attack model to compare the effects of CMD under different types of attack. Then, we use a dynamic game of incomplete information to determine the optimal strategy, which achieves the balance of the number of reconfiguration and security. Finally, experimental results show that our dual model reduces defensive costs while guarantees security.
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