In-Rack Test Tube Pose Estimation Using RGB-D Data
Hao Chen Weiwei Wan Masaki Matsushita Takeyuki Kotaka Kensuke Harada
Accurate robotic manipulation of test tubes in biology and medical industries
is becoming increasingly important to address workforce shortages and improve
worker safety. The detection and localization of test tubes are essential for
the robots to successfully manipulate test tubes. In this paper, we present a
framework to detect and estimate poses for the in-rack test tubes using color
and depth data. The methodology involves the utilization of a YOLO object
detector to effectively classify and localize both the test tubes and the tube
racks within the provided image data. Subsequently, the pose of the tube rack
is estimated through point cloud registration techniques. During the process of
estimating the poses of the test tubes, we capitalize on constraints derived
from the arrangement of rack slots. By employing an optimization-based
algorithm, we effectively evaluate and refine the pose of the test tubes. This
strategic approach ensures the robustness of pose estimation, even when
confronted with noisy and incomplete point cloud data.