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Contribution Details

Type Conference or Workshop Paper
Scope Discipline-based scholarship
Published in Proceedings Yes
Title Benefit of large field-of-view cameras for visual odometry
Organization Unit
Authors
  • Zichao Zhang
  • Henri Rebecq
  • Christian Forster
  • Davide Scaramuzza
Presentation Type paper
Item Subtype Original Work
Refereed Yes
Status Published in final form
Language
  • English
ISSN 1050-4729
Page Range 801 - 808
Event Title IEEE International Conference on Robotics and Automation (ICRA)
Event Type conference
Event Location Stockholm, Sweden
Event Start Date May 16 - 2016
Event End Date May 21 - 2016
Series Name IEEE International Conference on Robotics and Automation. Proceedings
Place of Publication Stockholm, Sweden
Publisher Institute of Electrical and Electronics Engineers
Abstract Text The transition of visual-odometry technology from research demonstrators to commercial applications naturally raises the question: “what is the optimal camera for vision-based motion estimation?” This question is crucial as the choice of camera has a tremendous impact on the robustness and accuracy of the employed visual odometry algorithm. While many properties of a camera (e.g. resolution, frame-rate, global-shutter/rolling-shutter) could be considered, in this work we focus on evaluating the impact of the camera field-of-view (FoV) and optics (i.e., fisheye or catadioptric) on the quality of the motion estimate. Since the motion-estimation performance depends highly on the geometry of the scene and the motion of the camera, we analyze two common operational environments in mobile robotics: an urban environment and an indoor scene. To confirm the theoretical observations, we implement a state-of-the-art VO pipeline that works with large FoV fisheye and catadioptric cameras. We evaluate the proposed VO pipeline in both synthetic and real experiments. The experiments point out that it is advantageous to use a large FoV camera (e.g., fisheye or catadioptric) for indoor scenes and a smaller FoV for urban canyon environments.
Related URLs
Digital Object Identifier 10.1109/ICRA.2016.7487210
Other Identification Number merlin-id:13324
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