Spatial non-cooperative target pose estimation method based on model and point cloud global matching

Abstract

The invention discloses a spatial non-cooperative target pose estimation method based on model and point clod global matching. The method comprises the steps that target scene point cloud is acquired by using a depth camera, the target scene point cloud acts as data point cloud to be registered after being filtered, and three-dimensional distance transformation is carried out on the target model point cloud; deblurring main directional transformation is carried out on the initial data point cloud to be registered and the target model point cloud, a translation domain is determined, search and registration are carried out in the translation domain and a rotation domain by using a global ICP algorithm, and an initial transformation matrix from a model coordinate system to a camera coordinate system is acquired, namely, the initial pose of a target is acquired; a pose transformation matrix of the pervious frame is enabled to act on data point cloud of the current frame, and registration with a model is carried out by using the ICP algorithm so as to acquire the pose of the current frame; and a rotation angle and a translation amount are calculated from the pose transformation matrix. The method disclosed by the invention has good anti-noise performance and an ability of outputting the target pose in real time, geometric features such as the normal and the curvature of the data point cloud are not required to be calculated, the registration speed is high, and the precision is high.

Description

technical field [0001] The invention relates to a space non-cooperative target pose acquisition technology, in particular to a space non-cooperative target pose estimation method based on model and point cloud global matching. Background technique [0002] The measurement of the relative position and attitude parameters of space objects is one of the important technologies for realizing relative navigation between spacecraft. The commonly used measurement sensors include monocular vision, binocular vision, radar, GPS, inertial navigation, etc. Vision-based position and attitude measurement The technology has the advantages of simple structure, stable performance, and good anti-interference, and has always been a hot topic of research. In the close-range stage, methods based on optical imaging and image processing outperform other methods. [0003] Space non-cooperative targets generally refer to space spacecraft that cannot provide effective cooperative information, whose s...

AI Technical Summary

Problems solved by technology

Since this method needs to identify the geometric features of non-cooperative targets, and the imaging of targets in the field of view may not have effective geometric features, it

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