Deep, spatially coherent Occupancy Maps based on Radar Measurements
This work addresses the need for precise environmental mapping in automotive applications, representing an incremental improvement by applying a novel method to a known bottleneck in radar-based perception.
The paper tackled the problem of accurately locating static objects for driver assistance by using neural networks to predict dense occupancy maps from sparse, uncertain radar inputs, achieving suitability for complex urban scenarios and large-scale mapping.
One essential step to realize modern driver assistance technology is the accurate knowledge about the location of static objects in the environment. In this work, we use artificial neural networks to predict the occupation state of a whole scene in an end-to-end manner. This stands in contrast to the traditional approach of accumulating each detection's influence on the occupancy state and allows to learn spatial priors which can be used to interpolate the environment's occupancy state. We show that these priors make our method suitable to predict dense occupancy estimations from sparse, highly uncertain inputs, as given by automotive radars, even for complex urban scenarios. Furthermore, we demonstrate that these estimations can be used for large-scale mapping applications.