Spatiotemporal alignment for low-level asynchronous data fusion with radar sensors in grid-based tracking and mapping
Spatiotemporal alignment for low-level asynchronous data fusion with radar sensors in grid-based tracking and mapping
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DOI：
10.1109/mfi.2016.7849494
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发表时间：
2016-09
期刊：
影响因子：
--
通讯作者：
Georg Tanzmeister;Sascha Steyer
中科院分区：
文献类型：
--
作者：
Georg Tanzmeister;Sascha Steyer
为了实现环境模型，通常需要融合来自多个传感器的数据，以满足实际应用的要求，特别是自动驾驶汽车的要求。低水平的传感器数据融合产生了潜在的优势，因为数据在用模型和假设解释之前被融合。然而，动态环境中精确融合所需的时空对准很困难，因为传感器通常无法同步。在这项工作中，提出了用于低级融合的异步传感器数据时空对齐的不同方法。重点关注雷达传感器，因为除了距离和方位之外，它们还可以测量径向速度。结果用于计算融合测量网格，以进行基于网格的跟踪和绘图。
Fusion of data from multiple sensors is often necessary to achieve an environment model, which meets the requirements of real-world applications, in particular those of autonomous vehicles. Sensor data fusion at a low-level yields potential advantages, as the data is fused before its interpretation with models and assumptions. However, spatiotemporal alignment, required for a precise fusion in dynamic environments, is difficult, as the sensors often cannot be synchronized. In this work, different approaches for spatiotemporal alignment of data from asynchronous sensors for low-level fusion are presented. Focus is given on radar sensors, as they allow measuring radial velocities in addition to range and bearing. The results are used to calculate fused measurement grids for grid-based tracking and mapping.