Scaling Local Control to Large-Scale Topological Navigation
This addresses scalability and reliability issues in visual topological navigation for robotics, representing an incremental improvement over existing methods.
The paper tackled the scalability and reliability challenges in visual topological navigation by accurately measuring local controller capabilities, achieving state-of-the-art results in trajectory following and planning in large-scale environments with generalization to real robots and new environments without retraining.
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.