In the unknown scenarios where satellite signals are obstructed,the visual-inertial fusion tech-nology can serve as an effective alternative to satellites,providing accurate navigation information for air-craft.However,in the low-altitude scene,traditional visual-inertial odometry(VIO)schemes face prob-lems such as inaccurate scale estimation,prone to divergence,and low accuracy.To address these prob-lems,a monocular VIO for low-altitude aircrafts with depth optimization supported by laser rangefinder and barometer is proposed to improve the accuracy of scale recovery.First,a robust initialization method of depth estimation based on baseline constraint and reference altitude is designed,where the length of the baseline is constrained to improve the accuracy of triangulation,and an initial depth selection mechanism supported by reference altitude is constructed for optimal depth selection.Then,a depth adaptive filling strategy based on the aircraft maneuvering state and plane assumption is introduced to suppress the posi-tioning divergence problem caused by scale loss.Finally,based on the multi-state constrained Kal-man filter(MSCKF),the position and attitude of the aircraft is updated by integrating the inertial information and the visual information modified by range measurements.To verify the proposed method,a UAV test platform equipped with visual/inertial/range sensors is constructed to perform test verification in a low-altitude scene.The result shows that compared to the traditional algorithm,the proposed method improves the positioning accuracy by an average of 61％.
维普
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