Towards visual ego-motion learning in robots
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1705.10279.pdf
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3.48 MB
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Adobe PDF
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Author(s)
Pillai, Sudeep
Leonard, John J
Date Issued
December 2017
Journal
2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
Pillai, Sudeep, and John J. Leonard. “Towards Visual Ego-Motion Learning in Robots.” 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 24-28 September, 2017, Vancouver, BC, Canada, IEEE, 2017. © 2017 IEEE
Version
Original manuscript
Abstract
Many model-based Visual Odometry (VO) algorithms have been proposed in the past decade, often restricted to the type of camera optics, or the underlying motion manifold observed. We envision robots to be able to learn and perform these tasks, in a minimally supervised setting, as they gain more experience. To this end, we propose a fully trainable solution to visual ego-motion estimation for varied camera optics. We propose a visual ego-motion learning architecture that maps observed optical flow vectors to an ego-motion density estimate via a Mixture Density Network (MDN). By modeling the architecture as a Conditional Variational Autoencoder (C-VAE), our model is able to provide introspective reasoning and prediction for ego-motion induced scene-flow. Additionally, our proposed model is especially amenable to bootstrapped ego-motion learning in robots where the supervision in ego-motion estimation for a particular camera sensor can be obtained from standard navigation-based sensor fusion strategies (GPS/INS and wheel-odometry fusion). Through experiments, we show the utility of our proposed approach in enabling the concept of self-supervised learning for visual ego-motion estimation in autonomous robots.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Massachusetts Institute of Technology. Department of Mechanical Engineering
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Creative Commons Attribution-Noncommercial-Share Alike
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DOI of Published Version
https://doi.org/10.1109/IROS.2017.8206441
