A 6D Pose Estimation Method for Indoor Target Objects Based on Enhanced Autoencoder

Abstract

The invention discloses a method for estimating the 6D pose of an indoor target object based on an enhanced self-encoder. The present invention is divided into three stages: multi-target object detection stage: first input a single color image to the improved version of Faster R-CNN, then the RPN network extracts the candidate frame, and then outputs the target category probability and two-dimensional bounding box through the full convolutional network ;Enhanced self-encoder to predict the key point stage of the object: use the probability expectation to connect the multi-target object detection stage and the enhanced self-encoder to predict the key point stage of the object, and encode and decode the region of interest by training the improved version of the stacked noise reduction autoencoder The noise-free region of interest of the same size, and then predict the key points of the target object on the two-dimensional image through the fully connected layer; calculate the 6D pose estimation stage of the target object: calculate the 6D pose of the target object according to the key points. The invention has strong robustness to the background clutter and occlusion of objects, is insensitive to light and color, and does not require objects to have rich texture features.

Description

technical field [0001] The invention relates to the field of attitude estimation, and specifically discloses a method for estimating the 6D attitude of an indoor target object based on an enhanced self-encoder. Background technique [0002] The target detection and object 6D pose of a single color image play a very important role in the human-computer interaction of industrial and mobile robot operations, virtual reality, and augmented reality. The occlusion problem is the most challenging in the 6D pose estimation problem. one of the problems. [0003] At present, the mainstream methods of pose estimation are mainly divided into template matching-based methods, point-based methods, descriptor-based methods, feature learning-based methods and convolutional neural network-based end-to-end methods. These methods are not very robust in dealing with occlusion problems in complex environments. [0004] The method based on template matching needs to do a lot of sampling work on ...

AI Technical Summary

Problems solved by technology

[0009] To sum up, the problems existing in the existing technology are: template matching-based methods are not ideal for occluded objects, and require subsequent complex processing; point-based methods and descriptor-based methods have higher requirements for point quality and texture features ; The method based on feature learning is difficult to deal with symmetrical objects and occluded objects; the end-to-end method based on convolutional neural network is not good at solving the occlusion of multiple targets in messy scenes and objects, and there are many follow-up processing, which cannot meet the practical application need

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