Predicting Topological Maps for Visual Navigation in Unexplored Environments
This addresses the challenge for robots to navigate efficiently in unseen environments by leveraging prior experiences, representing an incremental advancement in robotic learning systems.
The paper tackles the problem of autonomous visual navigation in unexplored environments by predicting probabilistic layout graphs, resulting in more rapid and accurate goal-based navigation compared to prior art, as demonstrated in Matterport3D with improved success and efficiency.
We propose a robotic learning system for autonomous exploration and navigation in unexplored environments. We are motivated by the idea that even an unseen environment may be familiar from previous experiences in similar environments. The core of our method, therefore, is a process for building, predicting, and using probabilistic layout graphs for assisting goal-based visual navigation. We describe a navigation system that uses the layout predictions to satisfy high-level goals (e.g. "go to the kitchen") more rapidly and accurately than the prior art. Our proposed navigation framework comprises three stages: (1) Perception and Mapping: building a multi-level 3D scene graph; (2) Prediction: predicting probabilistic 3D scene graph for the unexplored environment; (3) Navigation: assisting navigation with the graphs. We test our framework in Matterport3D and show more success and efficient navigation in unseen environments.