Method and device for quickly estimating 6D attitude of target object

By constructing the cube bag of the target object and fitting the penalty function, and combining it with the gradient descent algorithm, the 6D pose of the target object can be quickly estimated directly from RGB and depth images. This solves the problems of computational complexity and real-time performance in existing technologies and achieves efficient 6D pose estimation.

CN120976317AActive Publication Date: 2025-11-18BEIJING SHENMOU TECH CO LTD
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Patent Information

Application Number
CN202511104436.X
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-08-07
Publication Date
2025-11-18
Estimated Expiration
2045-08-07

AI Technical Summary

Technical Problem

Existing methods for 3D model pose estimation of target objects face challenges in terms of computational complexity, generalization, real-time performance, and computational cost, especially in meeting real-time requirements on edge devices.

Method used

By acquiring the RGB and depth images of the target object, a binary mask and a surface point cloud matrix are constructed. The gradient vectors of the center point and Euler angles of the target object are calculated using the cubic bag and the fitting penalty function. The 6D pose is then iteratively solved using the gradient descent algorithm, which simplifies the calculation process and improves accuracy and real-time performance.

Benefits of technology

It can quickly estimate 6D pose without using neural networks and detailed 3D models, reducing computation and latency, and improving real-time performance and accuracy on edge devices.

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Abstract

The invention provides a 6D attitude rapid estimation method and device for a target object, and relates to the field of electrical digital data processing in the technical field of machine vision and perception, and the method comprises the steps: obtaining an RGB image and a depth image of the target object, inputting the RGB image into a segmentation model, and obtaining a binary mask of the target object; acquiring a camera internal reference matrix, and constructing a curved surface point cloud matrix of the target object according to the depth image, the binary mask and the camera internal reference matrix; constructing a cubic package of the target object, and constructing a fitting penalty function according to the curved surface point cloud matrix and the cubic package; according to the curved surface point cloud matrix, the cubic package and the fitting penalty function, gradient vectors of the center point and the Euler angle of the target object are obtained through calculation; and performing iterative solution on the gradient vectors of the central point and the Euler angle through a gradient descent algorithm to obtain optimal solutions of the central point and the Euler angle, and taking the optimal solutions of the central point and the Euler angle as the 6D attitude of the target object.
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