Online depth calibration for RGB-D cameras using visual SLAM
Online depth calibration for RGB-D cameras using visual SLAM
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DOI：
10.1109/iros.2017.8206043
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发表时间：
2017-09
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影响因子：
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通讯作者：
Jan Quenzel;R. Rosu;Sebastian Houben;Sven Behnke
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文献类型：
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作者：
Jan Quenzel;R. Rosu;Sebastian Houben;Sven Behnke
现代消费类RGB-D相机价格实惠，并在高帧速率下提供密集的深度估计。因此，它们在构建密集环境表示时很受欢迎。然而，传感器往往不能提供准确的深度估计，因为工厂校准显示出静态变形。提出了一种利用视觉SLAM系统作为测量深度参考的在线深度校准方法。生成稀疏地图，并使用视觉信息来校正测量深度的静态变形，同时使用少量的薄板样条(TPS)来外推缺失数据。然后，可以使用校正后的深度来提高稀疏RGB-D图和3D环境重建的精度。随着更多的数据变得可用，深度校准会动态更新。我们的方法不依赖于平面几何体，如墙或颜色和深度相机之间的一对一像素对应。我们的方法在真实世界的场景中进行了评估，并根据地面真实数据进行了评估。与两种流行的自校准方法进行了比较。此外，我们的方法在聚集点云上显示出明显的视觉效果。
Modern consumer RGB-D cameras are affordable and provide dense depth estimates at high frame rates. Hence, they are popular for building dense environment representations. Yet, the sensors often do not provide accurate depth estimates since the factory calibration exhibits a static deformation. We present a novel approach to online depth calibration that uses a visual SLAM system as reference for the measured depth. A sparse map is generated and the visual information is used to correct the static deformation of the measured depth while missing data is extrapolated using a small number of thin plate splines (TPS). The corrected depth can then be used to improve the accuracy of the sparse RGB-D map and the 3D environment reconstruction. As more data becomes available, the depth calibration is updated on the fly. Our method does not rely on a planar geometry like walls or a one-to-one-pixel correspondence between color and depth camera. Our approach is evaluated in real-world scenarios and against ground truth data. Comparison against two popular self-calibration methods is performed. Furthermore, we show clear visual improvement on aggregated point clouds with our method.