Experimental Comparison of Two 6D Pose Estimation Algorithms in Robotic Fruit-Picking Tasks

被引:0
|
作者
Alterani, Alessio Benito [1 ]
Costanzo, Marco [1 ]
De Simone, Marco [1 ]
Federico, Sara [1 ]
Natale, Ciro [1 ]
机构
[1] Univ Campania Luigi Vanvitelli, Dipartimento Ingn, Via Roma 29, I-81031 Aversa, CE, Italy
关键词
pose estimation; robotic grasping; deep learning;
D O I
10.3390/robotics13090127
中图分类号
TP24 [机器人技术];
学科分类号
080202 ; 1405 ;
摘要
This paper presents an experimental comparison between two existing methods representative of two categories of 6D pose estimation algorithms nowadays commonly used in the robotics community. The first category includes purely deep learning methods, while the second one includes hybrid approaches combining learning pipelines and geometric reasoning. The hybrid method considered in this paper is a pipeline of an instance-level deep neural network based on RGB data only and a geometric pose refinement algorithm based on the availability of the depth map and the CAD model of the target object. Such a method can handle objects whose dimensions differ from those of the CAD. The pure learning method considered in this comparison is DenseFusion, a consolidated state-of-the-art pose estimation algorithm selected because it uses the same input data, namely, RGB image and depth map. The comparison is carried out by testing the success rate of fresh food pick-and-place operations. The fruit-picking scenario has been selected for the comparison because it is challenging due to the high variability of object instances in appearance and dimensions. The experiments carried out with apples and limes show that the hybrid method outperforms the pure learning one in terms of accuracy, thus allowing the pick-and-place operation of fruits with a higher success rate. An extensive discussion is also presented to help the robotics community select the category of 6D pose estimation algorithms most suitable to the specific application.
引用
收藏
页数:18
相关论文
共 50 条
  • [31] 6D Pose estimation and robotic arm grabbing based on minimum size points model
    Wu, Jichun
    Fang, Haiguo
    Yang, Guangxing
    Fan, Dapeng
    Jisuanji Jicheng Zhizao Xitong/Computer Integrated Manufacturing Systems, CIMS, 2022, 28 (08): : 2472 - 2480
  • [32] Survey on 6D Pose Estimation of Rigid Object
    Chen, Jiale
    Zhang, Lijun
    Liu, Yi
    Xu, Chi
    PROCEEDINGS OF THE 39TH CHINESE CONTROL CONFERENCE, 2020, : 7440 - 7445
  • [33] Orientation Keypoints for 6D Human Pose Estimation
    Fisch, Martin
    Clark, Ronald
    IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 2022, 44 (12) : 10145 - 10158
  • [34] 6D Pose Estimation for Bin-Picking based on Improved Mask R-CNN and DenseFusion
    Wang, Hesheng
    Situ, Huajie
    Zhuang, Chungang
    2021 26TH IEEE INTERNATIONAL CONFERENCE ON EMERGING TECHNOLOGIES AND FACTORY AUTOMATION (ETFA), 2021,
  • [35] 6D Pose Estimation Based on 3D Edge Binocular Reprojection Optimization for Robotic Assembly
    Li, Dong
    Mu, Quan
    Yuan, Yilin
    Wu, Shiwei
    Tian, Ye
    Hong, Hualin
    Jiang, Qian
    Liu, Fei
    IEEE ROBOTICS AND AUTOMATION LETTERS, 2023, 8 (12) : 8319 - 8326
  • [36] ACCURATE 6D OBJECT POSE ESTIMATION BY POSE CONDITIONED MESH RECONSTRUCTION
    Castro, Pedro
    Armagan, Anil
    Kim, Tae-Kyun
    2020 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH, AND SIGNAL PROCESSING, 2020, : 4147 - 4151
  • [37] A Pose Proposal and Refinement Network for Better 6D Object Pose Estimation
    Trabelsi, Ameni
    Chaabane, Mohamed
    Blanchard, Nathaniel
    Beveridge, Ross
    2021 IEEE WINTER CONFERENCE ON APPLICATIONS OF COMPUTER VISION WACV 2021, 2021, : 2381 - 2390
  • [38] PointPoseNet: Point Pose Network for Robust 6D Object Pose Estimation
    Chen, Wei
    Duan, Jinming
    Basevi, Hector
    Chang, Hyung Jin
    Leonardis, Ales
    2020 IEEE WINTER CONFERENCE ON APPLICATIONS OF COMPUTER VISION (WACV), 2020, : 2813 - 2822
  • [39] Deep instance segmentation and 6D object pose estimation in cluttered scenes for robotic autonomous grasping
    Wu, Yongxiang
    Fu, Yili
    Wang, Shuguo
    INDUSTRIAL ROBOT-THE INTERNATIONAL JOURNAL OF ROBOTICS RESEARCH AND APPLICATION, 2020, 47 (04): : 593 - 606
  • [40] RGB-Based Set Prediction Transformer of 6D Pose Estimation for Robotic Grasping Application
    Ning, Xiao
    Yang, Beining
    Huang, Si
    Zhang, Zhenzhe
    Pan, Binhui
    IEEE ACCESS, 2024, 12 : 138047 - 138060