移動ロボットによる非定常環境の階層的物体マップ学習
Learning Hierarchical Object Maps Of Non-Stationary Environments with mobile robots
オフィス環境で似た形状の物体をクラステンプレートと個別物体の2階層で表現し、EMアルゴリズムとベイズモデル選択で物体数・テンプレート数を推定する物体マップ学習手法を提案した。
著者: Dragomir Anguelov, Rahul Biswas, Daphne Koller, Benson Limketkai, Sebastian Thrun
分類: cs.LG, cs.RO, stat.ML
原文アブストラクト
Building models, or maps, of robot environments is a highly active research area; however, most existing techniques construct unstructured maps and assume static environments. In this paper, we present an algorithm for learning object models of non-stationary objects found in office-type environments. Our algorithm exploits the fact that many objects found in office environments look alike (e.g., chairs, recycling bins). It does so through a two-level hierarchical representation, which links individual objects with generic shape templates of object classes. We derive an approximate EM algorithm for learning shape parameters at both levels of the hierarchy, using local occupancy grid maps for representing shape. Additionally, we develop a Bayesian model selection algorithm that enables the robot to estimate the total number of objects and object templates in the environment. Experimental results using a real robot equipped with a laser range finder indicate that our approach performs well at learning object-based maps of simple office environments. The approach outperforms a previously developed non-hierarchical algorithm that models objects but lacks class templates.
