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Stereo and Active-Sensor Data Fusion for Improved Stereo Block Matching

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Part of the Lecture Notes in Computer Science book series (LNIP,volume 9730)

Abstract

This paper proposes an algorithm which uses the depth information acquired from an active sensor as guidance for a block matching stereo algorithm. In the proposed implementation, the disparity search interval used for the block matching is reduced around the depth values obtained from the active sensor, which leads to an improved matching quality and denser disparity maps and point clouds. The performance of the proposed method is evaluated by carrying out a series of experiments on 3 different data sets obtained from different robotic systems. We demonstrate with experimental results that the disparity estimation is improved and denser disparity maps are generated.

Keywords

  • Point Cloud
  • Active Sensor
  • Stereo Match
  • Stereo Camera
  • Block Match

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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  • DOI: 10.1007/978-3-319-41501-7_51
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Acknowledgement

This work was supported by the projects Patient@home and SAFE Perception which are funded by the Danish Innovation Fond.

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Correspondence to Stefan-Daniel Suvei .

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© 2016 Springer International Publishing Switzerland

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Suvei, SD. et al. (2016). Stereo and Active-Sensor Data Fusion for Improved Stereo Block Matching. In: Campilho, A., Karray, F. (eds) Image Analysis and Recognition. ICIAR 2016. Lecture Notes in Computer Science(), vol 9730. Springer, Cham. https://doi.org/10.1007/978-3-319-41501-7_51

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  • DOI: https://doi.org/10.1007/978-3-319-41501-7_51

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-41500-0

  • Online ISBN: 978-3-319-41501-7

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