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Chapter

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Title

Precision Vehicle Pose Estimation with Uncertainty-aware Neural Network

Authors

[ 1 ] Wydział Automatyki, Robotyki i Elektrotechniki, Politechnika Poznańska | [ 2 ] Instytut Robotyki i Inteligencji Maszynowej, Wydział Automatyki, Robotyki i Elektrotechniki, Politechnika Poznańska | [ SzD ] doctoral school student | [ P ] employee

Scientific discipline (Law 2.0)

[2.2] Automation, electronics, electrical engineering and space technologies

Year of publication

2024

Chapter type

chapter in monograph / paper

Publication language

english

Keywords
EN
  • monocular pose estimation
  • neural network architecture
  • uncertainty-aware pipeline
Abstract

EN This study presents a neural network designed for precise vehicle pose estimation from single images in complex settings. Utilising neural network backbones known for accurate human pose keypoint detection, our architecture effectively localises vehicle characteristic points. Task-specific modules estimate point coordinates and quality for pose computation. Training on ApolloCar3D with auto-generated 3D labels, our approach achieves the high pose estimation accuracy. We highlight the crucial role of accurate keypoint detection in addressing single-view geometry ambiguities, enhancing pose estimation precision.

Pages (from - to)

22 - 33

DOI

10.1007/978-3-031-70722-3_5

URL

https://link.springer.com/chapter/10.1007/978-3-031-70722-3_5

Book

Walking Robots into Real World. Proceedings of the CLAWAR 2024 Conference, Volume 1

Presented on

27th International Conference on Climbing and Walking Robots and the Support Technologies for Mobile Machines, CLAWAR 2024, 4-6.09.2024, Kaiserslautern, Germany

Ministry points / chapter

20

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