Research Article
Object Detection and Pose Estimation using Rotatable Object Detector DRBox-v2 for Bin-Picking Robot
@INPROCEEDINGS{10.4108/eai.5-10-2022.2326587, author={Eko Rudiawan Jamzuri and Agristia Riski Pinandita and Riska Analia and Susanto Susanto}, title={Object Detection and Pose Estimation using Rotatable Object Detector DRBox-v2 for Bin-Picking Robot}, proceedings={Proceedings of the 5th International Conference on Applied Engineering, ICAE 2022, 5 October 2022, Batam, Indonesia}, publisher={EAI}, proceedings_a={ICAE}, year={2023}, month={6}, keywords={rotatable object detection pose estimation drbox deep learning bin-picking}, doi={10.4108/eai.5-10-2022.2326587} }- Eko Rudiawan Jamzuri
Agristia Riski Pinandita
Riska Analia
Susanto Susanto
Year: 2023
Object Detection and Pose Estimation using Rotatable Object Detector DRBox-v2 for Bin-Picking Robot
ICAE
EAI
DOI: 10.4108/eai.5-10-2022.2326587
Abstract
This research aims to identify and estimate the object’s pose to support bin-picking robot perception. In this research, we proposed the usage of the ArUco marker as a visual landmark of the detection area. Furthermore, the image of the detection area is processed by rotatable object detector DRBox-v2 to get the object’s position and orientation in the camera frame. In the final process, the resulting DRBox-v2 position and orientation are transformed into a two-dimensional world coordinate as the final estimated pose. Based on the experimental result, the object detection yields an Average Precision (AP) of 0.54 while a threshold score of 0.5 is used. As the pose estimation result, the proposed method yields an average position error of 0.21 cm and a maximum position error of 0.28 cm. For the orientation error, the system achieves a maximum orientation error of about 1.23 degrees with an average orientation error of 0.58 degrees. This research contributes to the possibility of camera usage and end-to-end deep learning detector supporting bin-picking research.
