As demand for robotics manipulation application increases, accurate
vision-based 6D pose estimation becomes essential for autonomous operations.
Convolutional Neural Networks (CNNs) based approaches for pose estimation have
been previously introduced. However, the quest for better performance still
persists especially for accurate robotics manipulation. This quest extends to
the Agri-robotics domain. In this paper, we propose TransPose, an improved
Transformer-based 6D pose estimation with a depth refinement module. The
architecture takes in only an RGB image as input with no additional
supplementing modalities such as depth or thermal images. The architecture
encompasses an innovative lighter depth estimation network that estimates depth
from an RGB image using feature pyramid with an up-sampling method. A
transformer-based detection network with additional prediction heads is
proposed to directly regress the object's centre and predict the 6D pose of the
target. A novel depth refinement module is then used alongside the predicted
centers, 6D poses and depth patches to refine the accuracy of the estimated 6D
pose. We extensively compared our results with other state-of-the-art methods
and analysed our results for fruit-picking applications. The results we
achieved show that our proposed technique outperforms the other methods
available in the literature.

该研究提出了一种改进的基于 Transformer 的 6D 位姿估计方法 TransPose，它利用 RGB 图片作为输入，结合轻量级深度估计网络和变压器检测网络，并通过深度细化模块对位姿进行精度优化，在果树采摘应用方面取得了优于其他现有方法的效果。