The Journal of Korea Robotics Society (로봇학회논문지)

Korea Robotics Society (한국로봇학회)
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Development of an Efficient 3D Object Recognition Algorithm for Robotic Grasping in Cluttered Environments

혼재된 환경에서의 효율적 로봇 파지를 위한 3차원 물체 인식 알고리즘 개발

  • Received : 2022.05.24
  • Accepted : 2022.07.06
  • Published : 2022.08.31
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Abstract

3D object detection pipelines often incorporate RGB-based object detection methods such as YOLO, which detects the object classes and bounding boxes from the RGB image. However, in complex environments where objects are heavily cluttered, bounding box approaches may show degraded performance due to the overlapping bounding boxes. Mask based methods such as Mask R-CNN can handle such situation better thanks to their detailed object masks, but they require much longer time for data preparation compared to bounding box-based approaches. In this paper, we present a 3D object recognition pipeline which uses either the YOLO or Mask R-CNN real-time object detection algorithm, K-nearest clustering algorithm, mask reduction algorithm and finally Principal Component Analysis (PCA) alg orithm to efficiently detect 3D poses of objects in a complex environment. Furthermore, we also present an improved YOLO based 3D object detection algorithm that uses a prioritized heightmap clustering algorithm to handle overlapping bounding boxes. The suggested algorithms have successfully been used at the Artificial-Intelligence Robot Challenge (ARC) 2021 competition with excellent results.

Keywords

Acknowledgement

This work was partially supported by IITP grant funded by the Korea government (MSIT) (No. 2021-0-01202) and KIAT grant funded by the Korea Government (MOTIE) (P0008473, HRD Program for Industrial Innovation) This project was funded by Police-Lab 2.0 Program (www.kipot.or.kr) funded by the Ministry of Science and ICT(MSIT, Korea) & Korean National Police Agency (KNPA, Korea) (No. 082021D48000000) and This work was supported by Institute of Information & communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (MSIT) (No. 2021-0-01202)

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