publication . Preprint . 2019

DynaNet: Neural Kalman Dynamical Model for Motion Estimation and Prediction

Chen, Changhao; Lu, Chris Xiaoxuan; Wang, Bing; Trigoni, Niki; Markham, Andrew;
Open Access English
  • Published: 11 Aug 2019
Abstract
Dynamical models estimate and predict the temporal evolution of physical systems. State Space Models (SSMs) in particular represent the system dynamics with many desirable properties, such as being able to model uncertainty in both the model and measurements, and optimal (in the Bayesian sense) recursive formulations e.g. the Kalman Filter. However, they require significant domain knowledge to derive the parametric form and considerable hand-tuning to correctly set all the parameters. Data driven techniques e.g. Recurrent Neural Networks have emerged as compelling alternatives to SSMs with wide success across a number of challenging tasks, in part due to their a...
Subjects
free text keywords: Computer Science - Machine Learning, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Robotics, Statistics - Machine Learning
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43 references, page 1 of 3

[1] Michael Bloesch, Jan Czarnowski, Ronald Clark, Stefan Leutenegger, and Andrew J. Davison. CodeSLAM - Learning a Compact, Optimisable Representation for Dense Visual SLAM. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018.

[2] Michael Bloesch, Sammy Omari, Marco Hutter, and Roland Siegwart. Robust Visual Inertial Odometry Using a Direct EKF-Based Approach. In IEEE International Conference on Intelligent Robots and Systems, volume 2015-Decem, pages 298-304, 2015.

[3] Samarth Brahmbhatt, Jinwei Gu, Kihwan Kim, James Hays, and Jan Kautz. Geometry-Aware Learning of Maps for Camera Localization. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 2616-2625, 2018.

[4] Tayfun Çimen. State-Dependent Riccati Equation (SDRE) Control: A survey, volume 17. IFAC, 2008. [OpenAIRE]

[5] Ronald Clark, Sen Wang, Andrew Markham, Niki Trigoni, and Hongkai Wen. VidLoc: A Deep Spatio-Temporal Model for 6-DoF Video-Clip Relocalization. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017.

[6] Ronald Clark, Sen Wang, Hongkai Wen, Andrew Markham, and Niki Trigoni. VINet: VisualInertial Odometry as a Sequence-to-Sequence Learning Problem. In Association for the Advancement of Artificial Intelligence (AAAI), pages 3995-4001, 2017.

[7] Andrew J. Davison, Ian D. Reid, Nicholas D. Molton, and Olivier Stasse. MonoSLAM: RealTime Single Camera SLAM. IEEE Transactions on Pattern Analysis and Machine Intelligence, 29(6):1052-1067, 2007.

[8] D. G. Dudley. Dynamic system identification experiment design and data analysis. Proceedings of the IEEE, 67(7):1087-1087, July 1979.

[9] Jakob Engel, Thomas Schöps, and Daniel Cremers. LSD-SLAM: Large-Scale Direct Monocular SLAM. In European Conference on Computer Vision (ECCV), 2014. [OpenAIRE]

[10] Jakob Engel, Jurgen Sturm, and Daniel Cremers. Semi-Dense Visual Odometry for a Monocular Camera. In IEEE International Conference on Computer Vision (ICCV), pages 1449-1456, 2013.

[11] Christian Forster, Luca Carlone, Frank Dellaert, and Davide Scaramuzza. IMU Preintegration on Manifold for Efficient Visual-Inertial Maximum-a-Posteriori Estimation. In Robotics: Science and Systems, 2015. [OpenAIRE]

[12] Christian Forster, Matia Pizzoli, and Davide Scaramuzza. SVO: Fast Semi-Direct Monocular Visual Odometry. In IEEE International Conference on Robotics and Automation (ICRA), pages 15-22, 2014.

[13] Marco Fraccaro, Simon Kamronn, Ulrich Paquet, and Ole Winther. A Disentangled Recognition and Nonlinear Dynamics Model for Unsupervised Learning. In Advances in Neural Information Processing Systems (NIPS), 2017. [OpenAIRE]

[14] Marco Fraccaro, Søren Kaae Sønderby, Ulrich Paquet, and Ole Winther. Sequential Neural Models with Stochastic Layers. In Advances in Neural Information Processing Systems (NIPS), 2016. [OpenAIRE]

[15] A. Geiger, P. Lenz, C. Stiller, and R. Urtasun. Vision Meets Robotics: The KITTI Dataset. The International Journal of Robotics Research, 32(11):1231-1237, 2013.

43 references, page 1 of 3
Abstract
Dynamical models estimate and predict the temporal evolution of physical systems. State Space Models (SSMs) in particular represent the system dynamics with many desirable properties, such as being able to model uncertainty in both the model and measurements, and optimal (in the Bayesian sense) recursive formulations e.g. the Kalman Filter. However, they require significant domain knowledge to derive the parametric form and considerable hand-tuning to correctly set all the parameters. Data driven techniques e.g. Recurrent Neural Networks have emerged as compelling alternatives to SSMs with wide success across a number of challenging tasks, in part due to their a...
Subjects
free text keywords: Computer Science - Machine Learning, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Robotics, Statistics - Machine Learning
Download from
43 references, page 1 of 3

[1] Michael Bloesch, Jan Czarnowski, Ronald Clark, Stefan Leutenegger, and Andrew J. Davison. CodeSLAM - Learning a Compact, Optimisable Representation for Dense Visual SLAM. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018.

[2] Michael Bloesch, Sammy Omari, Marco Hutter, and Roland Siegwart. Robust Visual Inertial Odometry Using a Direct EKF-Based Approach. In IEEE International Conference on Intelligent Robots and Systems, volume 2015-Decem, pages 298-304, 2015.

[3] Samarth Brahmbhatt, Jinwei Gu, Kihwan Kim, James Hays, and Jan Kautz. Geometry-Aware Learning of Maps for Camera Localization. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 2616-2625, 2018.

[4] Tayfun Çimen. State-Dependent Riccati Equation (SDRE) Control: A survey, volume 17. IFAC, 2008. [OpenAIRE]

[5] Ronald Clark, Sen Wang, Andrew Markham, Niki Trigoni, and Hongkai Wen. VidLoc: A Deep Spatio-Temporal Model for 6-DoF Video-Clip Relocalization. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017.

[6] Ronald Clark, Sen Wang, Hongkai Wen, Andrew Markham, and Niki Trigoni. VINet: VisualInertial Odometry as a Sequence-to-Sequence Learning Problem. In Association for the Advancement of Artificial Intelligence (AAAI), pages 3995-4001, 2017.

[7] Andrew J. Davison, Ian D. Reid, Nicholas D. Molton, and Olivier Stasse. MonoSLAM: RealTime Single Camera SLAM. IEEE Transactions on Pattern Analysis and Machine Intelligence, 29(6):1052-1067, 2007.

[8] D. G. Dudley. Dynamic system identification experiment design and data analysis. Proceedings of the IEEE, 67(7):1087-1087, July 1979.

[9] Jakob Engel, Thomas Schöps, and Daniel Cremers. LSD-SLAM: Large-Scale Direct Monocular SLAM. In European Conference on Computer Vision (ECCV), 2014. [OpenAIRE]

[10] Jakob Engel, Jurgen Sturm, and Daniel Cremers. Semi-Dense Visual Odometry for a Monocular Camera. In IEEE International Conference on Computer Vision (ICCV), pages 1449-1456, 2013.

[11] Christian Forster, Luca Carlone, Frank Dellaert, and Davide Scaramuzza. IMU Preintegration on Manifold for Efficient Visual-Inertial Maximum-a-Posteriori Estimation. In Robotics: Science and Systems, 2015. [OpenAIRE]

[12] Christian Forster, Matia Pizzoli, and Davide Scaramuzza. SVO: Fast Semi-Direct Monocular Visual Odometry. In IEEE International Conference on Robotics and Automation (ICRA), pages 15-22, 2014.

[13] Marco Fraccaro, Simon Kamronn, Ulrich Paquet, and Ole Winther. A Disentangled Recognition and Nonlinear Dynamics Model for Unsupervised Learning. In Advances in Neural Information Processing Systems (NIPS), 2017. [OpenAIRE]

[14] Marco Fraccaro, Søren Kaae Sønderby, Ulrich Paquet, and Ole Winther. Sequential Neural Models with Stochastic Layers. In Advances in Neural Information Processing Systems (NIPS), 2016. [OpenAIRE]

[15] A. Geiger, P. Lenz, C. Stiller, and R. Urtasun. Vision Meets Robotics: The KITTI Dataset. The International Journal of Robotics Research, 32(11):1231-1237, 2013.

43 references, page 1 of 3
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