UAV Navigation Algorithm Based on Improved Algorithm of Simultaneous Localization and Mapping with Adaptive Local Range of Observations

The paper proposes an improved algorithm for the extended Kalman filter for simultaneous localization and mapping (EKF-SLAM), allowing the essential reduction of the amount of computation required by adapting the range of observation in real time in different threedimensional environments for unmanned aerial vehicles (UAVs). We built cloudy-point environment map and calculated coordinates of the characteristic points using 8-point normalized algorithm based on computer vision monocular. The improvement is achieved by adaptive dynamic restriction of the current dimensions of the environmental observable part and the number of observable targets for UAV positioning correction. The simulation results show that the proposed method significantly reduces the volume of calculations, while maintaining the accuracy of localization, and can be applied to the UAV navigation.

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