Scale-Aware Monocular Visual Odometry and Extrinsic Calibration Using Vehicle Kinematics

This paper proposes a new approach to scale-aware monocular visual odometry (VO) and extrinsic calibration using constraints on camera motion by vehicle kinematics. Main idea is to utilize the Ackermann steering model to observe absolute metric scale in turning motion. To describe motion of the camera attached to the vehicle, the authors first estimate unknown camera-vehicle relative pose by the proposed extrinsic calibration method. To stably observe scale, they detect turn regions and design an observer to estimate the absolute scale as a function of the camera rotation and direction of translational motion during turning. Using the observed scale, they propose an absolute scale recovery to estimate the unknown scale between turns. Because the proposed scale observer becomes singular near zero rotation, they conduct sensitivity analysis on the scale observer, and investigate appropriate conditions for stable scale estimation. For quantitative evaluation of the extrinsic calibration and the absolute scale recovery, they randomly generate synthetic driving datasets with various noise conditions, and evaluate the performance of each module statistically by Monte-Carlo simulations. To evaluate the overall performance, the authors implement their method and state-of-the-art monocular and stereo VO methods in the public outdoor driving KITTI dataset, and their method shows competitive scale recovery performance with no external sensor and no assumption on surroundings such as planar ground landmarks. To show promising applicability, they collect real-world driving datasets in two multi-floor underground parking lots, and demonstrate the accurate absolute scale recovery performance of their method in indoor driving situations.

• Record URL:
  • http://dx.doi.org/10.1109/TITS.2023.3309833
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/oclc/41297384
• Supplemental Notes:
  • Copyright © 2023, IEEE.
• Authors:
  • Kim, Changhyeon
  • Jang, Youngseok
  • Kim, Junha
  • Kim, Pyojin
  • Kim, H Jin
• Publication Date: 2023-12

Language

• English

Media Info

• Media Type: Web
• Features: References;
• Pagination: pp 14757-14771
• Serial:
  • IEEE Transactions on Intelligent Transportation Systems
  • Volume: 24
  • Issue Number: 12
  • 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: Autonomous vehicles; Calibration; Computer vision; Kinematics; Navigation systems; Odometers
• Subject Areas: Data and Information Technology; Highways; Vehicles and Equipment;

Filing Info

• Accession Number: 01911667
• Record Type: Publication
• Files: TRIS
• Created Date: Mar 12 2024 9:32AM