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Cross-based dense depth estimation by fusing stereo vision with measured sparse depth

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Abstract

Dense depth estimation is significant in robotic systems, such as for mapping, localization, and object recognition. For multiple sensors, an active depth sensor can provide accurate but sparse measurements for environments, and a camera pair can provide dense but imprecise stereo reconstruction results. In this paper, a tightly coupled fusion method is proposed for depth sensor and stereo camera to complete dense depth estimation, and advantages of the two type sensors are combined so as to achieve better depth estimation. An adaptive dynamic cross-arm algorithm are developed to integrate sparse depth measurements into camera-dominated semiglobal stereo matching. To obtain the optimal arm length for a measured pixel point, each cross-arm shape is variational and calculated automatically. Public datasets of KITTI, Middlebury, and Scene Flow datasets are used with comparison experiments to test performance of the proposed method, and real-world experiments are further conducted for verification.

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Notes

  1. https://github.com/TGUMobileVision/CrossArmFusion.

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Acknowledgements

This work was supported in part by the National Natural Science Foundation of China under Grant 61973234, in part by the Tianjin Natural Science Foundation under Grant 18JCZDJC96700 and Grant 20JCYBJC00180, and in part by Tianjin Science Fund for Distinguished Young Scholars under Grant 19JCJQJC62100.

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Authors and Affiliations

  1. School of Control Science and Engineering, Tianjin Key Laboratory of Advanced Technology of Electrical Engineering and Energy, Tiangong University, Tianjin, 300387, China

    Hongbao Mo, Baoquan Li & Wuxi Shi

  2. Institute of Robotics and Automatic Information System, Tianjin Key Laboratory of Intelligent Robotics, Nankai University, Tianjin, 300071, China

    Xuebo Zhang

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  1. Hongbao Mo
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Correspondence to Baoquan Li.

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Mo, H., Li, B., Shi, W. et al. Cross-based dense depth estimation by fusing stereo vision with measured sparse depth. Vis Comput 39, 4339–4350 (2023). https://doi.org/10.1007/s00371-022-02594-z

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