关键词: 姿态估计, 目标检测, 卷积神经网络, 特征提取

Abstract:

For the 3D pose estimation of the target, combined with the target detection model based on deep learning, 6D target pose estimation algorithm based on improved YOLO V2 is proposed. The feature information of an object in an RGB image is extracted by a convolutional neural network. Based on 2D detection, target position information is mapped to the three-dimensional space. The point-to-point mapping relationship is used to match and calculate target freedom degree in three dimensions. Then, target 6D pose is estimated. The algorithm detects a target in an RGB image. At the same time, target 6D attitude is predicted, which does not require additional post-processing. Experimental results show that the proposed algorithm performs better on other LineMod and Occlusion LineMod datasets than other CNN-based methods recently proposed. The proposed algorithm runs at 37?frames per second on Titan X GPU and can be processed in real time.

Key words: pose estimation, object detection, convolutional neural network, feature extraction