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ARM-VO: an efficient monocular visual odometry for ground vehicles on ARM CPUs

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Abstract

Localization is among the most important prerequisites for autonomous navigation. Vision-based systems have got great attention in recent years due to numerous camera advantages over other sensors. Reducing the computational burden of such systems is an active research area making them applicable to resource-constrained systems. This paper aims to propose and compare a fast monocular approach, named ARM-VO, with two state-of-the-art algorithms, LibViso2 and ORB-SLAM2, on Raspberry Pi 3. The approach is a sequential frame-to-frame scheme that extracts a sparse set of well-distributed features and tracks them in upcoming frames using Kanade–Lucas–Tomasi tracker. A robust model selection is used to avoid degenerate cases of fundamental matrix. Scale ambiguity is resolved by incorporating known camera height above ground. The method is open-sourced [https://github.com/zanazakaryaie/ARM-VO] and implemented in ROS mostly using NEON C intrinsics while exploiting the multi-core architecture of the CPU. Experiments on KITTI dataset showed that ARM-VO is 4–5 times faster and is the only method that can work almost real-time on Raspberry Pi 3. It achieves significantly better results than LibViso2 and is ranked second after ORB-SLAM2 in terms of accuracy.

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Notes

  1. We didn’t experience a parallel OpenCV with OpenMP on Raspberry Pi. It seems that only TBB is used internally to parallelize OpenCV codes (and not OpenMP).

  2. Stereo mode must be used because except LibViso2, other mentioned algorithms cannot recover scale in monocular mode.

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Authors and Affiliations

  1. The Faculty of Geodesy and Geomatics, K. N. Toosi University of Technology, Tehran, Iran

    Zana Zakaryaie Nejad & Ali Hosseininaveh Ahmadabadian

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  1. Zana Zakaryaie Nejad
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  2. Ali Hosseininaveh Ahmadabadian
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Correspondence to Ali Hosseininaveh Ahmadabadian.

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Zakaryaie Nejad, Z., Hosseininaveh Ahmadabadian, A. ARM-VO: an efficient monocular visual odometry for ground vehicles on ARM CPUs. Machine Vision and Applications 30, 1061–1070 (2019). https://doi.org/10.1007/s00138-019-01037-5

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