Unsupervised absolute scale calculation method and system

Abstract

The invention discloses an unsupervised absolute scale calculation method and a system. The method comprises the following steps of: judging a reference absolute scale depth map and a prediction depthmap by using a Generative Adversarial Network (GAN); and meanwhile, due to the constraint of the re-projection error, keeping the predicted depth map and the pose in the same scale, so that the posealso has the absolute scale.

Description

technical field [0001] The invention belongs to the field of visual odometry and depth estimation methods in the field of computer vision, in particular to an unsupervised absolute scale calculation method and system. Background technique [0002] In recent years, the algorithms of monocular dense depth estimation and visual odometer VO (Visual Odometry) based on deep learning methods have developed rapidly, and they are also key modules of SfM and SLAM systems. Existing studies have shown that VO and depth estimation based on supervised deep learning achieve good performance in many challenging environments and alleviate performance degradation issues such as scale drift. However, to train these supervised models in practical applications, it is difficult and expensive to obtain enough ground truth labeled data. In contrast, unsupervised methods have the great advantage of only requiring unlabeled video sequences. [0003] Deep unsupervised models for depth and pose estim...

AI Technical Summary

Problems solved by technology

However, most unsupervised monocular models face the following challenging problems: scale uncertainty and scale recovery problem

[0009] Such a scale recovery method is difficult to apply in practice, because there is no way to get the true value of each frame image in the actual scene

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