レーダー計測に基づく空間的に一貫した深い占有マップ
Deep, spatially coherent Occupancy Maps based on Radar Measurements
自動車レーダーの疎で不確実な計測から、ニューラルネットワークでシーン全体の占有状態をエンドツーエンドに推定し、大規模マッピングにも応用できることを示した論文。
著者: Daniel Bauer, Lars Kuhnert, Lutz Eckstein
分類: cs.RO, cs.AI
原文アブストラクト
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.
