6IMPOSE:在6D中弥合现实差距,用于机器人掌握的姿势估计
Hongpeng Cao1, Lukas Dirnberger1, Daniele Bernardini1,2
1School of Engineering and Design, Technical University of Munich, Munich, Germany.
Frontiers in robotics and AI
|October 13, 2023
概括
本研究介绍了6IMPOSE,这是一个用于生成合成数据和执行6D姿势估计的框架. 它使机器人能够在87%的成功率下抓住家用物品,从而提高了现实世界的性能.
科学领域:
- 机器人技术 机器人技术 机器人技术
- 计算机视觉 计算机视觉
- 机器学习 机器学习
背景情况:
- 6D姿势识别对于机器人掌握至关重要.
- 当前的深度学习模型与现实世界的概括性作斗争.
研究的目的:
- 介绍6IMPOSE,这是一个用于模拟到真实数据生成和6D姿势估计的新框架.
- 增强现实世界机器人应用的6D姿势估计模型的概括能力.
主要方法:
- 使用Blender用于合成RGBD图像数据集生成与6D姿势注释.
- 开发了一个实时的,两阶段的6D姿势估计方法,集成YOLO-V4和精简的PVN3D.
- 实现域随机化和微调数据生成,以改进模拟到真实传输.
主要成果:
- 生成了五个家庭对象的合成RGBD数据集.
- 在从杂乱的场景中抓住各种各样的家用物品时,取得了87%的成功率.
- 成功地将训练有素的模型转移到机器人掌握实验中.
结论:
- 6IMPOSE框架有效地弥合了6D姿势估计的模拟与真实差距.
- 优化的推断管道和数据生成克服了现有算法的概括限制.
- 开发的代码库,数据集和模型是公开的,以促进进一步的研究.
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