局所制御の能力測定で大規模トポロジカルナビゲーションを実現
Scaling Local Control to Large-Scale Topological Navigation
局所コントローラの能力を正確に測ることで、大規模環境でもスケーラブルで堅牢な視覚的トポロジカルナビゲーションを実現する手法を提案した論文。
著者: Xiangyun Meng, Nathan Ratliff, Yu Xiang, Dieter Fox
分類: cs.RO
原文アブストラクト
Visual topological navigation has been revitalized recently thanks to the advancement of deep learning that substantially improves robot perception. However, the scalability and reliability issue remain challenging due to the complexity and ambiguity of real world images and mechanical constraints of real robots. We present an intuitive solution to show that by accurately measuring the capability of a local controller, large-scale visual topological navigation can be achieved while being scalable and robust. Our approach achieves state-of-the-art results in trajectory following and planning in large-scale environments. It also generalizes well to real robots and new environments without retraining or finetuning.
