Global Pose Estimation with an Attention-Based Recurrent Network
Open accessAbstract
The ability for an agent to localize itself within an environment is crucial for many real-world applications. For unknown environments, Simultaneous Localization and Mapping (SLAM) enables incremental and concurrent building of and localizing within a map. We present a new, differentiable architecture, Neural Graph Optimizer, progressing towards a complete neural network solution for SLAM by designing a system composed of a local pose estimation model, a novel pose selection module, and a novel graph optimization process. The entire architecture is trained in an end-to-end fashion, enabling the network to automatically learn domain-specific features relevant to the visual odometry and avoid the involved process of feature engineering. We demonstrate the effectiveness of our system on a simulated 2D maze and the 3D ViZ-Doom environment.
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A differentiable architecture named neural graph optimizer is proposed for global pose graph optimization, and together with a local pose estimation model it achieves a complete end-to-end neural network solution for SLAM.
“Recently, a differentiable architecture named neural graph optimizer is proposed for global pose graph optimization [136]. Together with a local pose estimation model, it achieves a complete end-to-end neural network solution for SLAM.”
From Empowering things with intelligence: a survey of the progress, challenges, and opportunities in artificial intelligence of things (2021) · cites this paper as [136]
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ORB-SLAM: A Versatile and Accurate Monocular SLAM System
Mur-Artal, R., Montiel, J. M. M., & Tardos, J. D. (2015) · IEEE Transactions on Robotics · doi:10.1109/tro.2015.2463671 · Cited by 6,778 · see citing works
Flownet: Learning optical flow with convolutional networks
Dosovitskiy, A., Fischer, P., Ilg, E., Hausser, P., Hazirbas, C., Golkov, V., Smagt, P. V. D., Cremers, D., & Brox, T. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.316 · Cited by 4,313 · see citing works
LSD-SLAM: Large-scale direct monocular SLAM
Engel, J., Schöps, T., & Cremers, D. (2014) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-10605-2_54 · Cited by 3,851 · see citing works
iSAM2: Incremental smoothing and mapping using the Bayes tree
Kaess, M., Johannsson, H., Roberts, R., Ila, V., Leonard, J. J., & Dellaert, F. (2012) · The International Journal of Robotics Research · doi:10.1177/0278364911430419 · Cited by 1,362 · see citing works
Deepvo: towards end-to-end visual odometry with deep recurrent convolutional neural networks
Wang, S., Clark, R., Wen, H., & Trigoni, N. (2017) · 2017 IEEE International Conference on Robotics and Automation (ICRA) · doi:10.1109/icra.2017.7989236 · Cited by 970 · see citing works
Neural Map: Structured Memory for Deep Reinforcement Learning
Parisotto, E., & Salakhutdinov, R. (2017) · arXiv · doi:10.48550/arxiv.1702.08360 · Cited by 178 · see citing works
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Zhang, J., & Tao, D. (2021) · IEEE Internet of Things Journal · doi:10.1109/jiot.2020.3039359 · Cited by 774 · see citing works
Self-citationDeepMapping: Unsupervised Map Estimation From Multiple Point Clouds
Ding, L., & Feng, C. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00885 · Cited by 79 · see citing works
- Authors
- Parisotto, E., Chaplot, D. S., Zhang, J., & Salakhutdinov, R.
- Affiliations
- Apple (United States) (US)
- Venue
- 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
- Publisher
- IEEE
- Published
- 2018-06
- Pages
- 350-35009
- OpenAlex
- W2788688180
- Cited by
- 88
- References
- 6
- Citations by year
- 2026: 1 · 2025: 5 · 2024: 3 · 2023: 14 · 2022: 10 · 2021: 10 · 2020: 19 · 2019: 19 · 2018: 7
- FWCI
- 23.87
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Engineering › Aerospace Engineering › Robotics and Sensor-Based Localization
- Physical Sciences › Computer Science › Artificial Intelligence › Reinforcement Learning in Robotics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Vision and Imaging
- Keywords
- global pose estimation, SLAM, pose selection, graph optimization, visual odometry, end-to-end learning
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