Related papers: Predicting Topological Maps for Visual Navigation in Unexplored Environments

Predicting Topological Maps for Visual Navigation in Unexplored Environments

URL: http://arxiv.org/abs/2211.12649v1
Date: Wed, 23 Nov 2022 00:53:11 GMT
Title: Predicting Topological Maps for Visual Navigation in Unexplored Environments
Authors: Huangying Zhan, Hamid Rezatofighi, Ian Reid
Abstract summary: We propose a robotic learning system for autonomous exploration and navigation in unexplored environments. The core of our method is a process for building, predicting, and using probabilistic layout graphs for assisting goal-based visual navigation. We test our framework in Matterport3D and show more success and efficient navigation in unseen environments.
Score: 28.30219170556201
License: http://creativecommons.org/licenses/by-nc-sa/4.0/
Abstract: 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.

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