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Efficient velocity estimation for MAVs by fusing motion from two frontally parallel cameras

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Abstract

Efficient velocity estimation is crucial for the robust operation of navigation control loops of micro aerial vehicles (MAVs). Motivated by the research on how animals exploit their visual topographies to rapidly perform locomotion, we propose a bio-inspired method that applies quasi-parallax technique to estimate the velocity of an MAV equipped with a forward-looking stereo camera without GPS. Different to the available optical flow-based methods, our method can realize efficient metric velocity estimation without applying any depth information from either additional distance sensors or from stereopsis. In particular, the quasi-parallax technique, which claims to press maximal benefits from the configuration of two frontally parallel cameras, leverages pairs of parallel visual rays to eliminate rotational flow for translational velocity estimation, followed by refinement of the estimation of rotational velocity and translational velocity iteratively and alternately. Our method fuses the motion information from two frontal-parallel cameras without performing correspondences matching, achieving enhanced robustness and efficiency. Extensive experiments on synthesized and actual scenes demonstrate the effectiveness and efficiency of our method.

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Notes

  1. For our closed loop trajectory, the ending location coincides with the starting location. Thus DiffRatio = 0 ideally.

  2. We put several objects on the ground to make it non-planar, as shown in Fig. 10d.

  3. As a rule of thumb in the control community, high-frequency changes of input could be unexpectedly magnified by control units, resulting in unpredictable controlling performance.

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Authors and Affiliations

  1. Temasek Laboratories, National University of Singapore, Singapore, 117411, Singapore

    Zhi Gao, Bharath Ramesh, Pengfei Wang & Xu Yan

  2. Advanced Digital Sciences Center (ADSC), Singapore, 138632, Singapore

    Wen-Yan Lin

  3. Department of Computer Science, School of Informatics, Huazhong Agricultural University, Wuhan, 430070, China

    Ruifang Zhai

Authors
  1. Zhi Gao
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  2. Bharath Ramesh
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  3. Wen-Yan Lin
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  4. Pengfei Wang
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  5. Xu Yan
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  6. Ruifang Zhai
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Correspondence to Zhi Gao.

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Gao, Z., Ramesh, B., Lin, WY. et al. Efficient velocity estimation for MAVs by fusing motion from two frontally parallel cameras. J Real-Time Image Proc 16, 2367–2378 (2019). https://doi.org/10.1007/s11554-018-0752-5

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  • DOI: https://doi.org/10.1007/s11554-018-0752-5

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