Target pose estimation method and system based on attention mechanism and Hough voting

Abstract

The invention discloses a target pose estimation method and system based on an attention mechanism and Hough voting. The method comprises the following steps: acquiring a color image and a depth image; segmenting and cutting the color image to obtain color and depth image blocks of each target object; using two strategies to estimate the six-dimensional pose of a target object, for a three-dimensional rotation matrix, based on a feature extraction network of bidirectional space attention, performing robust feature extraction by utilizing target surface two-dimensional feature constraint, and then using a multi-layer sensing network to regress the three-dimensional rotation matrix of the target; and for a three-dimensional translation vector, reconstructing a target object point cloud and normalizing point cloud data, estimating a point cloud three-dimensional translation direction vector point by point by adopting a Hough voting network, finally establishing a translation center straight line set, and solving a space nearest point to obtain a target three-dimensional translation vector. According to the method, the rotation matrix and the translation vector are estimated respectively, the execution speed is high, and precision is high.

Description

technical field [0001] The invention relates to the fields of robot visual perception and computer vision, in particular to a target pose estimation method and system based on attention mechanism and Hough voting. Background technique [0002] Object pose estimation refers to identifying known objects in the current scene from the perspective of the camera, and estimating their 3-axis orientation and 3-axis position in the camera's 3-dimensional space coordinate system, more specifically, referring to the object The rigid body transformation matrix T of the 3D model transformed from its own coordinate system to the camera coordinate system consists of a 3D rotation matrix R and a 3D translation vector t, which constitute the 6D pose P of the object. Object pose estimation is a key content in robot scene understanding. Using computer vision technology has achieved a series of results in the fields of robot grasping, human-computer interaction and augmented reality, and has be...

AI Technical Summary

Problems solved by technology

However, due to the need to artificially calculate templates or feature points for specific objects, the robustness is poor and the process is cumbersome, and such methods are also easily affected by background or occlusion, and the accuracy is low.

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