Sparse direct method-based monocular visual odometry (VO) method of quadrotor unmanned-aerial-vehicle

Abstract

The invention discloses a sparse direct method-based monocular visual odometry (VO) method of a quadrotor unmanned-aerial-vehicle. The method is characterized by: carrying out depth estimation on a key frame, wherein feature points of the key frame are determined by a feature point method, an eigenmatrix between two adjacent frames is calculated, the eigenmatrix is decomposed and a rotation matrix and a translation matrix between the two adjacent frames are calculated to obtain an external parameter matrix, and then depths of the feature points are calculated according to a triangulation method; and after obtaining depth values of the feature points, obtaining the pose of the quadrotor unmanned-aerial-vehicle by solving through a sparse-matrix direct method, and carrying out motion estimation on all frames, wherein sparse feature points are extracted, the direct method is used to calculate a position of each feature point in the next frame, and grayscale information of each pixel point in pixel blocks which have a fixed size and are around the feature points is utilized to optimize grayscale differences between the two adjacent frames to obtain the motion pose of a camera. The method has the advantages that cumulative errors are avoided, higher accuracy is maintained for a long period, and the calculation amount can also be reduced.

Description

technical field [0001] The invention relates to the technical field of navigation and positioning of unmanned aerial vehicles, in particular to a method for measuring the range of unmanned aerial vehicle monocular vision. Background technique [0002] The real-time pose data of the quadrotor UAV is the premise of positioning and control. The process of a carrier carrying a single or multiple cameras using only its image input to estimate its own motion is called Visual Odometry (VO). Visual odometry incrementally estimates the vehicle pose by perceiving changes in the input image. The effective operation of the visual odometry algorithm requires sufficient lighting in the environment and rich enough scene textures. [0003] Monocular vision odometry only uses a single camera as input, the system configuration is simple, and the ability to adapt to environmental scale changes is stronger than that of multi-eye vision systems. The existing monocular vision odometry methods ...

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Problems solved by technology

However, computing the photometric error is more computationally intensive than computing the reprojection error since it involves the entire image area

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