μ-MAR: Multiplane 3D Marker based Registration for depth-sensing cameras
μ-MAR: Multiplane 3D Marker based Registration for depth-sensing cameras
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DOI：
10.1016/j.eswa.2015.08.011
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发表时间：
2015-12
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通讯作者：
Marcelo Saval-Calvo;J. Azorín-López;Andrés Fuster-Guilló;H. Mora-Mora-H.-Mora-Mora-1404169548
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文献类型：
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作者：
Marcelo Saval-Calvo;J. Azorín-López;Andrés Fuster-Guilló;H. Mora-Mora-H.-Mora-Mora-1404169548
许多应用包括物体重建，机器人引导，和。场景映射需要对一个场景的多个视图进行配准，从而生成一个完整的场景几何和外观模型。在实际情况下，视图之间的转换是未知的，有必要应用专家推理来估计它们。在过去的几年里，低成本的深度传感相机的出现加强了对这一主题的研究，激发了大量的新应用。尽管它们在许多应用中具有足够的分辨率和精度，但由于所提供数据的信噪比（SNR）和分辨率，某些情况可能无法用一般的最先进的配准方法解决。一般来说，任何3D系统都可能出现处理低信噪比数据的问题，因此有必要在这方面提出新的解决方案。在本文中，我们提出了一种μ-MAR方法，该方法能够将低成本深度感测相机提供的3D点的粗配准集和精细配准集转换到一个共同的坐标系中，尽管它并不局限于这些传感器。该方法采用基于模型的多平面配准方法，克服了数据噪声问题。具体来说，它迭代注册由多个平面组成的3D标记，这些平面是从场景的多个视图中提取的。由于标记和感兴趣的对象在场景中是静态的，因此将为标记获得的转换应用于对象以重建它。利用合成数据和实际数据进行了实验。综合数据可以通过目测和豪斯多夫距离分别进行定性和定量评价。用Primesense Carmine RGB-D传感器采集的数据进行了实际数据实验，验证了该方法的有效性。这种方法已与几种最先进的方法进行了比较。结果表明，μ- mar在有噪声情况下的目标配准精度优于现有的配准方法。
Many applications including object reconstruction, robot guidance, and. scene mapping require the registration of multiple views from a scene to generate a complete geometric and appearance model of it. In real situations, transformations between views are unknown and it is necessary to apply expert inference to estimate them. In the last few years, the emergence of low-cost depth-sensing cameras has strengthened the research on this topic, motivating a plethora of new applications. Although they have enough resolution and accuracy for many applications, some situations may not be solved with general state-of-the-art registration methods due to the signal-to-noise ratio (SNR) and the resolution of the data provided. The problem of working with low SNR data, in general terms, may appear in any 3D system, then it is necessary to propose novel solutions in this aspect. In this paper, we propose a method,μ-MAR, able to both coarse and fine register sets of 3D points provided by low-cost depth-sensing cameras, despite it is not restricted to these sensors, into a common coordinate system. The method is able to overcome the noisy data problem by means of using a model-based solution of multiplane registration. Specifically, it iteratively registers 3D markers composed by multiple planes extracted from points of multiple views of the scene. As the markers and the object of interest are static in the scenario, the transformations obtained for the markers are applied to the object in order to reconstruct it. Experiments have been performed using synthetic and real data. The synthetic data allows a qualitative and quantitative evaluation by means of visual inspection and Hausdorff distance respectively. The real data experiments show the performance of the proposal using data acquired by a Primesense Carmine RGB-D sensor. The method has been compared to several state-of-the-art methods. The results show the good performance of theμ-MAR to register objects with high accuracy in presence of noisy data outperforming the existing methods.