A Behavioral Approach to Visual Navigation with Graph Localization Networks

By Kevin Chen, Juan Pablo de Vicente, Gabriel Sepulveda, Á. Soto, Marynel Vázquez, Silvio Savarese. 2019

109 citations recorded by the source corpus.

Inspired by research in psychology, we introduce a behavioral approach for visual navigation using topological maps. Our goal is to enable a robot to navigate from one location to another, relying only on its visual input and the topological map of the environment. We propose using graph neural networks for localizing the agent in the map, and decompose the action space into primitive behaviors implemented as convolutional or recurrent neural networks. Using the Gibson simulator, we verify that our approach outperforms relevant baselines and is able to navigate in both seen and unseen environments.

Subfields: Computer Vision and Pattern Recognition

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In Computer Vision and Pattern Recognition

Citations in the corpus

Bayesian Relational Memory for Semantic Visual Navigation (2019, methodology): “Additionally, thanks to a limited set of K targets, we adopt a behavior approach [8] for improved performances: we train a separate policy μi(st; θi) for each semantic target Ti and when given the sub-goal g from the BRM module, we directly execute its corresponding behavior network.”
Combining Optimal Control and Learning for Visual Navigation in Novel Environments (2019, background): “Several works also use end-to-end learning for navigation using laser scans [12, 13, 14], for training and combining a local planner with a higher level roadmap for long range navigation [15, 16, 17, 18], or visual servoing [19].”
Open-vocabulary Queryable Scene Representations for Real World Planning (2022, background): “Recently, topological maps [17], [18] and scene graphs [19], [20] emerge as an effective discrete representation of scenes.”
Learning Object Relation Graph and Tentative Policy for Visual Navigation (2020, methodology): “The methods [23,5,22] propose to use both visual observation features and the topological guidance of scenes.”
iGibson, a Simulation Environment for Interactive Tasks in Large Realistic Scenes (2020, background): “learn to control physical interactions [1, 2], navigate based on sensor signals [3, 4, 5, 6, 7], or plan long horizon tasks [8, 9, 10, 11].”
Neural Topological SLAM for Visual Navigation (2020, background): “This includes spatial memories [17, 30] and topological approaches [8, 13, 34, 35, 42, 45].”