Six-degree-of-freedom pose estimation algorithm based on projection point coordinate regression

Abstract

The invention discloses a six-degree-of-freedom pose estimation algorithm based on projection point coordinate regression, Based on the single-step target detection algorithm, this paper extends the BB8 algorithm to solve the problem of pose estimation. Using the BB8 algorithm for reference, the six-degree-of-freedom pose parameters are calculated by EPnP algorithm for the projection coordinates of the vertices of the three-dimensional boundary box for every object of interest detected. The convolution neural network provided by the invention can carry out end-to-end training and prediction for the pose estimation target, the computational efficiency and the precision of pose estimation are improved, In the multi-object scene, only one EPnP calculation is needed for each object instance detected, and the algorithm does not need to run repeatedly for many times, thus the running time of the algorithm is saved. The algorithm can achieve the current optimal precision of pose estimation without post-processing of pose correction, and can realize real-time processing.

Description

technical field [0001] The present invention relates to the technical field of digital image processing, and more specifically relates to the digital image processing technology of three-dimensional stereoscopic vision. Background technique [0002] Determining the three-dimensional translation and rotation transformation parameters (a total of six degrees of freedom) of an object in an image relative to a camera is a classic problem in the field of computer vision. In recent years, the development of emerging applications such as augmented reality, autonomous driving, and visual robots has required higher accuracy and speed for six-degree-of-freedom pose estimation, and academia has also conducted a lot of research work on this demand. In terms of the form of input data, existing algorithms can be mainly divided into pose estimation algorithms based on RGB images and pose estimation algorithms based on RGBD data. Among them, the pose estimation algorithm based on RGBD data...

AI Technical Summary

Problems solved by technology

[0006] The BB8 algorithm uses a multi-step processing method, resulting in low computational efficiency; multiple convolutional neural networks are trained separately, and cannot be comprehensively tuned for the pose estimation target, resulting in low pose estimation accuracy; the pose correction process involves image Rendering and other operations have high computational complexity, which leads to a slow running speed of the algorithm; for scenes with multiple objects, the BB8 algorithm needs to perform separate calculations for each object instance, resulting in a significant increase in the running time of the algorithm

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