A visual localization approach for dynamic objects based on hybrid semantic-geometry information is presented. Due to the interference of moving objects in the real environment, the traditional simultaneous localization and mapping (SLAM) system can be corrupted. To address this problem, we propose a method for static/dynamic image segmentation that leverages semantic and geometric modules, including optical flow residual clustering, epipolar constraint checks, semantic segmentation, and outlier elimination. We integrated the proposed approach into the state-of-the-art ORB-SLAM2 and evaluated its performance on both public datasets and a quadcopter platform. Experimental results demonstrated that the root-mean-square error of the absolute trajectory error improved, on average, by 93.63% in highly dynamic benchmarks when compared with ORB-SLAM2. Thus, the proposed method can improve the performance of state-of-the-art SLAM systems in challenging scenarios.
A moving model of close target in a certain velocity is established aiming at the characteristic of low maneuverability. The Extended kalman filter (EKF) is used to reduce the error in location tracking. From the simulation result, it can be concluded that the moving model can describe the moving characteristic of close ballistic target perfectly. The Extended kalman filter can reduce the error in tracking location clearly.
Adaptive function projective synchronization between two different chaotic system with unknown parameters is studied Based on Lyapunov stability theory, using the adaptive control method, all uncertain parameters including unknown coefficients of nonlinear terms of the drive system and response system are identified. Taking the single-mode laser Lorenz system and single scroll attractor chaotic system as examples, numerical simulations with fourth-order Runge-Kutta method are presented to verify the effectiveness of the proposed scheme.
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