In-Rack Test Tube Pose Estimation Using RGB-D Data

TL;DR

This paper introduces a framework for accurately detecting and estimating the poses of test tubes within racks using RGB-D data, combining object detection, point cloud registration, and optimization for robustness.

Contribution

It presents a novel integrated approach that leverages YOLO detection, rack constraints, and optimization to improve pose estimation of test tubes in complex environments.

Findings

Robust pose estimation achieved even with noisy data

Effective classification and localization of test tubes and racks

Enhanced accuracy over traditional methods

Abstract

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.