Pacific-Rim Symposium on Image and Video Technology

Image and Video Technology pp 27-37 | Cite as

Multi-frame Feature Integration for Multi-camera Visual Odometry

  • Hsiang-Jen Chien
  • Haokun Geng
  • Chia-Yen Chen
  • Reinhard Klette
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9431)

Abstract

State-of-the-art ego-motion estimation approaches in the context of visual odometry (VO) rely either on Kalman filters or bundle adjustment. Recently proposed multi-frame feature integration (MFI [1]) techniques aim at finding a compromise between accuracy and computation efficiency. In this paper we generalise an MFI algorithm towards the full use of multi-camera-based visual odometry for achieving more consistent ego-motion estimation in a parallel scalable manner. A series of experiments indicated that the generalised integration technique contributes to an improvement of above 70 % over our direct VO implementation, and further improved the monocular MFI technique by more than 20 %.

Keywords

Visual odometry Ego-motion estimation Feature tracking 

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Hsiang-Jen Chien
    • 1
  • Haokun Geng
    • 2
  • Chia-Yen Chen
    • 3
  • Reinhard Klette
    • 1
  1. 1.School of EngineeringAuckland University of TechnologyAucklandNew Zealand
  2. 2.Department of Computer ScienceUniversity of AucklandAucklandNew Zealand
  3. 3.Department of Computer Science and Information EngineeringNational University of KaohsiungKaohsiungTaiwan

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