用深度学习识别桌面上物品并估算姿态，让机器人自动整理桌面。
Detection, Recognition and Pose Estimation of Tabletop Objects
- 基于深度神经网络实现物品检测与类别识别
- 精准预测物品相对于参考轴的角度朝向
- 适合需要自动化整理场景的机器人研发人员
使用深度神经网络解决杂乱桌面清理问题，是社交与工业机器人领域的重要课题。本项目聚焦于社会应用，开发了一种能够检测和识别常见桌面物体（如马克杯、鼠标、订书机）的神经网络模型，并预测这些物体在桌面上相对于某一参考轴的摆放角度。假设每个物体都有一个固定的预期位置和朝向，可通过深度学习模型预测的姿态计算出将物体从初始位置移至目标位置的变换矩阵，进而输入到抓取放置机器人中执行操作。本文详细介绍了该项目所采用的深度学习方法，用于物体检测与姿态估计。
原文摘要 · Abstract (English)
The problem of cleaning a messy table using Deep Neural Networks is a very interesting problem in both social and industrial robotics. This project focuses on the social application of this technology. A neural network model that is capable of detecting and recognizing common tabletop objects, such as a mug, mouse, or stapler is developed. The model also predicts the angle at which these objects are placed on a table,with respect to some reference. Assuming each object has a fixed intended position and orientation on the tabletop, the orientation of a particular object predicted by the deep learning model can be used to compute the transformation matrix to move the object from its initial position to the intended position. This can be fed to a pick and place robot to carry out the transfer.This paper talks about the deep learning approaches used in this project for object detection and orientation estimation.
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