Probabilistic multi-sensor fusion based on signed distance functions
Probabilistic multi-sensor fusion based on signed distance functions
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DOI：
10.1109/icra.2016.7487333
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发表时间：
2016-05
期刊：
影响因子：
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通讯作者：
Vincent Dietrich;Dong Chen;Kai M. Wurm;Georg von Wichert;Philipp Ennen
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文献类型：
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作者：
Vincent Dietrich;Dong Chen;Kai M. Wurm;Georg von Wichert;Philipp Ennen
在本文中，我们提出了一种方法的概率融合的三维传感器测量。我们的融合算法是基于截断符号距离函数。它通过使用随机变量对表面进行建模来显式地考虑测量噪声。此外，我们提出的表面模型提供了一个明确的估计的空间不确定性。该方法可以在GPU上实现，以实现高更新性能并实现模型的在线更新。该方法进行了评估，在模拟和使用真实的传感器数据。在我们的实验中，我们证实，它准确地估计表面从嘈杂的传感器数据，它提供了相应的估计的不确定性。我们还可以证明，该方法能够融合来自具有不同噪声特性的传感器的测量结果。
In this paper, we present an approach for the probabilistic fusion of 3D sensor measurements. Our fusion algorithm is based on truncated signed distance functions. It explicitly considers the measurement noise by modeling the surface using random variables. Furthermore, our proposed surface model provides an explicit estimation of the spatial uncertainty. The approach can be implemented on a GPU to achieve a high update performance and enable online updates of the model. The approach was evaluated in simulation and using real sensor data. In our experiments, we confirmed that it accurately estimates surfaces from noisy sensor data and that it provides a corresponding estimate of the uncertainty. We could also show that the approach is able to fuse measurements from sensors with different noise characteristics.