Despite the attention marker-less pose estimation has attracted in recent
years, marker-based approaches still provide unbeatable accuracy under
controlled environmental conditions. Thus, they are used in many fields such as
robotics or biomedical applications but are primarily implemented through
classical approaches, which require lots of heuristics and parameter tuning for
reliable performance under different environments. In this work, we propose
MarkerPose, a robust, real-time pose estimation system based on a planar target
of three circles and a stereo vision system. MarkerPose is meant for
high-accuracy pose estimation applications. Our method consists of two deep
neural networks for marker point detection. A SuperPoint-like network for
pixel-level accuracy keypoint localization and classification, and we introduce
EllipSegNet, a lightweight ellipse segmentation network for sub-pixel-level
accuracy keypoint detection. The marker's pose is estimated through stereo
triangulation. The target point detection is robust to low lighting and motion
blur conditions. We compared MarkerPose with a detection method based on
classical computer vision techniques using a robotic arm for validation. The
results show our method provides better accuracy than the classical technique.
Finally, we demonstrate the suitability of MarkerPose in a 3D freehand
ultrasound system, which is an application where highly accurate pose
estimation is required. Code is available in Python and C++ at
this https URL

该研究提出了 MarkerPose，一个基于三个圆形平面目标和立体视觉系统的强韧实时姿态估计系统，其包括用于 marker 点检测的像素级准确键点定位和分类的 SuperPoint 网络以及用于亚像素级准确键点检测的轻量级椭圆分割网络。该方法通过立体测量来估计 marker 的姿态，具有更高的精度和抗噪声性能，适用于需要高精度姿态估计的 3D 自由手超声系统中。