都市環境のセマンティックマップから人間の動きの占有事前分布を学習する
Learning Occupancy Priors of Human Motion from Semantic Maps of Urban Environments
都市環境のセマンティック情報のみを入力として、歩行者の将来の占有マップを予測するCNNベースの手法を提案し、従来の逆最適制御法と比較して優れた性能を示した。
著者: Andrey Rudenko, Luigi Palmieri, Johannes Doellinger, Achim J. Lilienthal, Kai O. Arras
分類: cs.RO
原文アブストラクト
Understanding and anticipating human activity is an important capability for intelligent systems in mobile robotics, autonomous driving, and video surveillance. While learning from demonstrations with on-site collected trajectory data is a powerful approach to discover recurrent motion patterns, generalization to new environments, where sufficient motion data are not readily available, remains a challenge. In many cases, however, semantic information about the environment is a highly informative cue for the prediction of pedestrian motion or the estimation of collision risks. In this work, we infer occupancy priors of human motion using only semantic environment information as input. To this end we apply and discuss a traditional Inverse Optimal Control approach, and propose a novel one based on Convolutional Neural Networks (CNN) to predict future occupancy maps. Our CNN method produces flexible context-aware occupancy estimations for semantically uniform map regions and generalizes well already with small amounts of training data. Evaluated on synthetic and real-world data, it shows superior results compared to several baselines, marking a qualitative step-up in semantic environment assessment.
