Pose Estimation of Point Sets Using Residual MLP in Intelligent Transportation Infrastructure

6D pose estimation of arbitrary objects is a crucial topic for intelligent transportation infrastructure measurement. However, some external environmental factors and the characteristics of the object itself impact the accuracy of the object’s pose estimation in practical applications. In this paper, the authors propose a new multi-class dataset ICD-4 (Industrial car Components Dataset) for 6D object pose estimation, which mainly includes four component categories, and every category takes 20,000 different scenarios. ICD-4 dataset delivers quite a few research challenges involving the range of object pose transformations and has significant research value for small-scale pose estimation tasks. They also propose an innovative method PoseMLP, a pose estimation network that uses residual MLP (multilayer perceptron) modules to predict the 6D pose estimation directly. Simultaneously, the experimental results demonstrate the effectiveness and reliability of the proposed method.

• Record URL:
  • https://doi.org/10.1109/TITS.2023.3250604
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/oclc/41297384
• Supplemental Notes:
  • Copyright © 2023, IEEE.
• Authors:
  • Li, Yujie
  • Yin, Zhiyun
  • Zheng, Yuchao
  • Lu, Huimin
  • T. Kamiya
  • Nakatoh, Yoshihisa
  • Serikawa, Seichi
• Publication Date: 2023-11

Language

• English

Media Info

• Media Type: Web
• Features: References;
• Pagination: pp 13359-13369
• Serial:
  • IEEE Transactions on Intelligent Transportation Systems
  • Volume: 24
  • Issue Number: 11
  • Publisher: Institute of Electrical and Electronics Engineers (IEEE)
  • ISSN: 1524-9050
  • Serial URL: http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=6979

Subject/Index Terms

• TRT Terms: Datasets; Infrastructure; Intelligent transportation systems; Mobile robots
• Subject Areas: Data and Information Technology; Highways; Vehicles and Equipment;

Filing Info

• Accession Number: 01911670
• Record Type: Publication
• Files: TRIS
• Created Date: Mar 12 2024 9:32AM