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首页> 外文期刊>IEEE sensors journal >Robust Robot Pose Estimation for Challenging Scenes With an RGB-D Camera
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Robust Robot Pose Estimation for Challenging Scenes With an RGB-D Camera

机译：使用RGB-D摄像机对场景提出挑战的鲁棒机器人姿势估计

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摘要

Rigid robot pose estimation with an RGB-D camera has attracted substantial research attention recently for the reason that the RGB-D camera can capture RGB and depth information simultaneously. Despite the huge progress that has been made, there are still some unresolved issues like the pose estimation in texture-less or structure-less scenes. Aiming at this problem, this paper presents a robust real-time pose estimation method with an RGB-D camera for texture-less and structure-less scenes. Our contributions are threefold. First, we present an improved ORB algorithm for extracting reliable inliers, in which adaptive threshold setting method of FAST corners decision is proposed for extracting sufficient keypoints. In addition, an effective inliers refinement method, based on motion smoothness consistency constraint, is introduced for obtaining fine inliers. Second, based on the characteristics of RGB-D camera, this paper proposes a novel hybrid reprojection errors optimization model (HREOM) to estimate pose by concurrently minimizing 3D-3D and 3D-2D reprojection errors. Third, we carry out comprehensive experiments on TUM public datasets to demonstrate the robustness, accuracy, and real-time of the proposed system. The quantitative evaluations show that our system can extract sufficient inliers in thaw extreme scenes. Furthermore, our method performs as good as or better than other state-of-the-art solutions. Notably, our system can operate in the texture-less and structure-less environment, while other methods are prone to failure.
机译：由于RGB-D相机可以同时捕获RGB和深度信息，因此使用RGB-D相机进行刚性机器人姿势估计已引起了广泛的研究关注。尽管已经取得了巨大的进步，但是仍然存在一些未解决的问题，例如在无纹理或无结构的场景中的姿势估计。针对这个问题，本文提出了一种鲁棒的实时姿态估计方法，该方法利用RGB-D摄像机处理无纹理和无结构的场景。我们的贡献是三倍。首先，我们提出了一种改进的ORB算法，用于提取可靠的inliers，其中提出了一种FAST角点决策的自适应阈值设置方法，用于提取足够的关键点。此外，引入了一种基于运动平滑一致性约束的有效的inliers细化方法，以获取精细的inliers。其次，根据RGB-D相机的特点，提出一种新颖的混合重投影误差优化模型（HREOM），通过同时最小化3D-3D和3D-2D重投影误差来估计姿势。第三，我们对TUM公共数据集进行了全面的实验，以证明所提出系统的鲁棒性，准确性和实时性。定量评估表明，我们的系统可以在融化的极端场景中提取足够的像素。此外，我们的方法的性能与其他最新解决方案一样好或更好。值得注意的是，我们的系统可以在无纹理和无结构的环境中运行，而其他方法则容易出错。

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  • 来源
    《IEEE sensors journal》 |2019年第6期|2217-2229|共13页
  • 作者

    Yu Hongshan; Fu Qiang; Yang Zhengeng; Tan Lei; Sun Wei; Sun Mingui;

  • 作者单位

    Hunan Univ, Natl Engn Lab Robot Visual Percept & Control, Changsha 410006, Hunan, Peoples R China;

    Hunan Univ, Natl Engn Lab Robot Visual Percept & Control, Changsha 410006, Hunan, Peoples R China;

    Hunan Univ, Natl Engn Lab Robot Visual Percept & Control, Changsha 410006, Hunan, Peoples R China;

    Hunan Univ, Natl Engn Lab Robot Visual Percept & Control, Changsha 410006, Hunan, Peoples R China;

    Hunan Univ, Natl Engn Lab Robot Visual Percept & Control, Changsha 410006, Hunan, Peoples R China;

    Univ Pittsburgh, Lab Computat Neurosci, Pittsburgh, PA 15260 USA;

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  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    RGB-D camera; feature extractor and matcher; pose estimation; reprojection error; challenging scenes;

    机译：RGB-D相机;特征提取和匹配器;姿势估计;重投影误差;充满挑战的场景;

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