Neural Topological SLAM for Visual Navigation

Devendra Singh Chaplot
Ruslan Salakhutdinov
Abhinav Gupta
Saurabh Gupta
Published at CVPR, 2020


This paper studies the problem of image-goal navigation which involves navigating to the location indicated by a goal image in a novel previously unseen environment. To tackle this problem, we design topological representations for space that effectively leverage semantics and afford approximate geometric reasoning. At the heart of our representations are nodes with associated semantic features, that are interconnected using coarse geometric information. We describe supervised learning-based algorithms that can build, maintain and use such representations under noisy actuation. Experimental study in visually and physically realistic simulation suggests that our method builds effective representations that capture structural regularities and efficiently solve long-horizon navigation problems. We observe a relative improvement of more than 50% over existing methods that study this task.

Neural Topological SLAM

The Neural Topological SLAM model consists of 3 components, a Graph Construction module which updates the topological map as it receives observations, a Global Policy which samples subgoals, and a Local Policy which takes navigational actions to reach the subgoal.

Short Presentation

Paper and Bibtex


Chaplot, D.S., Salakhutdinov, R., Gupta, A. and Gupta, S. 2020. Neural Topological SLAM for Visual Navigation. In CVPR.

  title={Neural Topological SLAM for Visual Navigation},
  author={Chaplot, Devendra Singh and Salakhutdinov, Ruslan and 
          Gupta, Abhinav and Gupta, Saurabh},

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This work was supported by IARPA DIVA D17PC00340, US Army W911NF1920104, ONR Grant N000141812861, ONR MURI, ONR Young Investigator, DARPA MCS and Apple. We would also like to acknowledge NVIDIA’s GPU support.
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