Santos Vitor, Rato Daniela, Dias Paulo, Oliveira Miguel
DEM/IEETA, University of Aveiro, 3810-193 Aveiro, Portugal.
DETI/IEETA, University of Aveiro, 3810-193 Aveiro, Portugal.
Sensors (Basel). 2020 Nov 24;20(23):6717. doi: 10.3390/s20236717.
Systems composed of multiple sensors for exteroceptive perception are becoming increasingly common, such as mobile robots or highly monitored spaces. However, to combine and fuse those sensors to create a larger and more robust representation of the perceived scene, the sensors need to be properly registered among them, that is, all relative geometric transformations must be known. This calibration procedure is challenging as, traditionally, human intervention is required in variate extents. This paper proposes a nearly automatic method where the best set of geometric transformations among any number of sensors is obtained by processing and combining the individual pairwise transformations obtained from an experimental method. Besides eliminating some experimental outliers with a standard criterion, the method exploits the possibility of obtaining better geometric transformations between all pairs of sensors by combining them within some restrictions to obtain a more precise transformation, and thus a better calibration. Although other data sources are possible, in this approach, 3D point clouds are obtained by each sensor, which correspond to the successive centers of a moving ball its field of view. The method can be applied to any sensors able to detect the ball and the 3D position of its center, namely, LIDARs, mono cameras (visual or infrared), stereo cameras, and TOF cameras. Results demonstrate that calibration is improved when compared to methods in previous works that do not address the outliers problem and, depending on the context, as explained in the results section, the multi-pairwise technique can be used in two different methodologies to reduce uncertainty in the calibration process.
由多个用于外部感知的传感器组成的系统正变得越来越普遍，例如移动机器人或受到高度监控的空间。然而，为了组合和融合这些传感器以创建对感知场景的更大、更稳健的表示，传感器之间需要进行适当的配准，也就是说，所有相对几何变换都必须已知。这种校准过程具有挑战性，因为传统上需要不同程度的人工干预。本文提出了一种近乎自动的方法，通过处理和组合从一种实验方法获得的各个成对变换，来获得任意数量传感器之间的最佳几何变换集。除了使用标准准则消除一些实验异常值外，该方法还利用了在某些限制内组合所有传感器对之间的变换以获得更精确变换从而实现更好校准的可能性。尽管可能有其他数据源，但在这种方法中，每个传感器获取3D点云，这些点云对应于移动球在其视野中的连续中心。该方法可应用于任何能够检测球及其中心3D位置的传感器，即激光雷达、单目相机（视觉或红外）、立体相机和飞行时间相机。结果表明，与之前未解决异常值问题的工作方法相比，校准得到了改进，并且根据结果部分所解释的情况，多成对技术可用于两种不同的方法中，以减少校准过程中的不确定性。
