Spatiotemporal alignment for low-level asynchronous data fusion with radar sensors in grid-based tracking and mapping

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.