The DOI matches its Crossref record; 29 of the 29 references it cites were found in a registry or an index.
DeepVO: Towards end-to-end visual odometry with deep Recurrent Convolutional Neural Networks
Open accessAbstract
This paper studies monocular visual odometry (VO) problem. Most of existing VO algorithms are developed under a standard pipeline including feature extraction, feature matching, motion estimation, local optimisation, etc. Although some of them have demonstrated superior performance, they usually need to be carefully designed and specifically fine-tuned to work well in different environments. Some prior knowledge is also required to recover an absolute scale for monocular VO. This paper presents a novel end-to-end framework for monocular VO by using deep Recurrent Convolutional Neural Networks (RCNNs). Since it is trained and deployed in an end-to-end manner, it infers poses directly from a sequence of raw RGB images (videos) without adopting any module in the conventional VO pipeline. Based on the RCNNs, it not only automatically learns effective feature representation for the VO problem through Convolutional Neural Networks, but also implicitly models sequential dynamics and relations using deep Recurrent Neural Networks. Extensive experiments on the KITTI VO dataset show competitive performance to state-of-the-art methods, verifying that the end-to-end Deep Learning technique can be a viable complement to the traditional VO systems.
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“More surprisingly, they show that the welltrained networks enable scale-consistent predictions over long videos, while the accuracy is still inferior to traditional methods because of ignoring geometric information.”
From DF-VO: What Should Be Learnt for Visual Odometry? (2021) · cites this paper as Wang et al. 2017
Experimental — accuracy not yet measuredThe cited source says what the claim attributes to it.
In the citing paper
“On the opposing side, end-to-end approaches can estimate the 6 degrees of freedom (DoF) poses directly from a sequence of RGB images, that is, it does not need engineering effort to design the modules in the traditional VO pipeline [4].”
From Transformer-Based Model for Monocular Visual Odometry: A Video Understanding Approach (2023) · cites this paper as [4]
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In the citing paper
“This problem is called monocular visual odometry and often relies on geometric approaches that require considerable engineering effort for a specific scenario.”
From Transformer-Based Model for Monocular Visual Odometry: A Video Understanding Approach (2023) · cites this paper as [4]
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In the citing paper
“the amount of crater detections that ODN-detector produces is robust to disturbances or modifications of the image quality due to Gaussian noise, shot noise, brightness levels, as well as different illumination conditions.”
From Optical navigation for Lunar landing based on Convolutional Neural Network crater detector (2022) · cites this paper as [16]
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Citations
Cites
Very Deep Convolutional Networks for Large-Scale Image Recognition
Simonyan, K., & Zisserman, A. (2014) · arXiv · doi:10.48550/arxiv.1409.1556 · Cited by 114,148 · cited 1×
“There have been some popular and powerful DNN architectures, such as VGGNet [22] and GoogLeNet [23], developed for computer vision tasks, producing remarkable performance.”
MethodGoing deeper with convolutions
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., & Rabinovich, A. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298594 · Cited by 48,562
Are we ready for autonomous driving? The KITTI vision benchmark suite
Geiger, A., Lenz, P., & Urtasun, R. (2012) · 2012 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2012.6248074 · Cited by 14,897 · cited 2×
“In this section, we evaluate the proposed end-to-end monocular VO approach on the well-known KITTI VO/SLAM benchmark [3].”
BackgroundMethod(2016) · Cited by 11,679
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 · cited 1×
“Examples include keyframe base PTAM [9] and ORB-SLAM [10].”
BackgroundParallel Tracking and Mapping for Small AR Workspaces
Klein, G., & Murray, D. (2007) · 2007 6th IEEE and ACM International Symposium on Mixed and Augmented Reality · doi:10.1109/ismar.2007.4538852 · Cited by 4,346 · cited 1×
“Examples include keyframe base PTAM [9] and ORB-SLAM [10].”
BackgroundFlownet: 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 · cited 2×
“In order to reduce both the training time and data required to converge, the CNN is based on a pre-trained FlowNet model [24].”
MethodMonoSLAM: Real-time single camera SLAM
Davison, A. J., Reid, I. D., Molton, N. D., & Stasse, O. (2007) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2007.1049 · Cited by 3,959 · cited 1×
“To mitigate this problem, visual Simultaneous Localisation and Mapping (SLAM) or Structure from Motion (SfM) can be adopted to maintain a feature map for drift correction along with pose estimation [8].”
MethodORB-SLAM: A Versatile and Accurate Monocular SLAM System
Mur-Artal, R., Montiel, J. M. M., & Tardos, J. D. (2015) · Zaguan (Universidad de Zaragoza) · Cited by 3,153
Multiple view geometry in computer vision
Opower, H. (2002) · Optics and Lasers in Engineering · doi:10.1016/s0143-8166(01)00145-2 · Cited by 3,044
Engel, J. J., Koltun, V., & Cremers, D. (2017) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2017.2658577 · Cited by 2,995
PoseNet: A Convolutional Network for Real-Time 6-DOF Camera Relocalization
Kendall, A., Grimes, M., & Cipolla, R. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.336 · Cited by 2,489
SVO: Fast semi-direct monocular visual odometry
Forster, C., Pizzoli, M., & Scaramuzza, D. (2014) · 2014 IEEE International Conference on Robotics and Automation (ICRA) · doi:10.1109/icra.2014.6906584 · Cited by 2,205
DTAM: Dense tracking and mapping in real-time
Newcombe, R. A., Lovegrove, S. J., & Davison, A. J. (2011) · 2011 International Conference on Computer Vision · doi:10.1109/iccv.2011.6126513 · Cited by 2,078 · cited 1×
“Direct methods, in contrast, are capable of exploiting all the pixels in consecutive images for pose estimation under the assumption of photometric consistency, e.g., DTAM in [11].”
MethodTowards End-To-End Speech Recognition with Recurrent Neural Networks
Graves, A., & Jaitly, N. (2014) · Cited by 1,861
Scaramuzza, D., & Fraundorfer, F. (2011) · IEEE Robotics & Automation Magazine · doi:10.1109/mra.2011.943233 · Cited by 1,550 · cited 2×
“1, a classic pipeline [1], [2], which typically consists of camera calibration, feature detection, feature matching (or tracking), outlier rejection (e.”
BackgroundLong-Term Recurrent Convolutional Networks for Visual Recognition and Description
Donahue, J., Hendricks, L. A., Rohrbach, M., Venugopalan, S., Guadarrama, S., Saenko, K., & Darrell, T. (2017) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2016.2599174 · Cited by 1,547
StereoScan: Dense 3d reconstruction in real-time
Geiger, A., Ziegler, J., & Stiller, C. (2011) · IEEE Intelligent Vehicles Symposium · doi:10.1109/ivs.2011.5940405 · Cited by 1,119 · cited 3×
“1, employ multi-view geometry [5] to determine motion after extracting and matching (or tracking) salient feature points from a sequence of images, such as the algorithm in [6] and LIBVISO2 [7].”
MethodLong-term Recurrent Convolutional Networks for Visual Recognition and Description
Donahue, J., Hendricks, L. A., Guadarrama, S., Rohrbach, M., Venugopalan, S., Saenko, K., & Darrell, T. (2014) · Defense Technical Information Center (DTIC) · doi:10.21236/ada623249 · Cited by 1,101
Nistér, D., Naroditsky, O., & Bergen, J. R. (2004) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2004.1315094 · Cited by 1,031
Visual Odometry : Part II: Matching, Robustness, Optimization, and Applications
Fraundorfer, F., & Scaramuzza, D. (2012) · IEEE Robotics & Automation Magazine · doi:10.1109/mra.2012.2182810 · Cited by 620 · cited 1×
“1, a classic pipeline [1], [2], which typically consists of camera calibration, feature detection, feature matching (or tracking), outlier rejection (e.”
BackgroundSemi-dense Visual Odometry for a Monocular Camera
Engel, J. J., Sturm, J., & Cremers, D. (2013) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2013.183 · Cited by 521 · cited 1×
“Recently, semi-direct approaches which realise superior performance are developed for the monocular VO [12]– [14].”
BackgroundPlace Recognition with ConvNet Landmarks: Viewpoint-Robust, Condition-Robust, Training-Free
Sünderhauf, N., Rowlands, S., Jacobson, A., Dayoub, F., Pepperell, E., Upcroft, B., & Milford, M. J. (2015) · Robotics: Science and Systems · doi:10.15607/rss.2015.xi.022 · Cited by 374
Exploring Representation Learning With CNNs for Frame-to-Frame Ego-Motion Estimation
Costante, G., Mancini, M., Valigi, P., & Ciarfuglia, T. A. (2016) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2015.2505717 · Cited by 203 · cited 1×
“To overcome this problem, the CNNs are provided with dense optical flow instead of RGB images for motion estimation in [21].”
MethodLearning Visual Odometry with a Convolutional Network
Konda, K., & Memisevic, R. (2015) · International Conference on Computer Vision Theory and Applications · doi:10.5220/0005299304860490 · Cited by 170 · cited 1×
“To our knowledge, [19] firstly realises DL based VO through synchrony detection between image sequences and features.”
MethodConvolutional networks for real-time 6-DOF camera relocalization.Preprint
Kendall, A., Grimes, M. K., & Cipolla, R. (2015) · arXiv (Cornell University) · Cited by 50
Evaluation of non-geometric methods for visual odometry
Ciarfuglia, T. A., Costante, G., Valigi, P., & Ricci, E. (2014) · Robotics and Autonomous Systems · doi:10.1016/j.robot.2014.08.001 · Cited by 50 · cited 1×
“Optical flow is used to train K Nearest Neighbour (KNN), Gaussian Process (GP) and Support Vector Machines (SVM) regression algorithms for the monocular VO in [15], [16] and [17], respectively.”
MethodMemory-based learning for visual odometry
Roberts, R. B., Nguyen, H., Krishnamurthi, N., & Balch, T. R. (2008) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/robot.2008.4543185 · Cited by 49 · cited 1×
“Optical flow is used to train K Nearest Neighbour (KNN), Gaussian Process (GP) and Support Vector Machines (SVM) regression algorithms for the monocular VO in [15], [16] and [17], respectively.”
MethodSemi-parametric learning for visual odometry
Guizilini, V., & Ramos, F. T. (2013) · The International Journal of Robotics Research · doi:10.1177/0278364912472245 · Cited by 20 · cited 1×
“Optical flow is used to train K Nearest Neighbour (KNN), Gaussian Process (GP) and Support Vector Machines (SVM) regression algorithms for the monocular VO in [15], [16] and [17], respectively.”
Method
Research works printed but not linked: 1
- zaremba, 2014, Learning to execute
Cited by
Sorted by citations
A survey of uncertainty in deep neural networks
Gawlikowski, J., Tassi, C. R. N., Ali, M., Lee, J., Humt, M., Feng, J., Kruspe, A., Triebel, R., Jung, P., Roscher, R., et al. (2023) · Artificial Intelligence Review · doi:10.1007/s10462-023-10562-9 · Cited by 1,670
“[207], [330], [331] developed deep learning based localization systems along with uncertainty estimates.”
BackgroundZhan, H., Garg, R., Weerasekera, C. S., Li, K., Agarwal, H., & Reid, I. M. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00043 · Cited by 705
UnDeepVO: Monocular Visual Odometry Through Unsupervised Deep Learning
Li, R., Wang, S., Long, Z., & Gu, D. (2018) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2018.8461251 · Cited by 596
Self-citationDeep Learning Sensor Fusion for Autonomous Vehicle Perception and Localization: A Review
Fayyad, J., Jaradat, M. A., Gruyer, D., & Najjaran, H. (2020) · Sensors · doi:10.3390/s20154220 · Cited by 559
“To demonstrate the advantage of adopting a recurrent neural network for VO, Wang et al. [154] implemented an end-to-end deep recurrent convolutional network that takes sequential RGB images and detects poses.”
CubeSLAM: Monocular 3-D Object SLAM
Yang, S., & Scherer, S. (2019) · IEEE Transactions on Robotics · doi:10.1109/tro.2019.2909168 · Cited by 511
“Recently, there is also some end-to-end deep learning-based SLAM without object representations, such as DVSO [28], DeepVO [29].”
Background
Metadata
- Authors
- Wang, S., Clark, R., Wen, H., & Trigoni, N.
- Affiliations
- University of Oxford (GB)
- Venue
- 2017 IEEE International Conference on Robotics and Automation (ICRA)
- Publisher
- IEEE
- Published
- 2017-05
- Pages
- 2043-2050
- ISSN
- 1050-4729 (ISSN-L)
- OpenAlex
- W2598706937
- Cited by
- 970
- References
- 29
- Citations by year
- 2026: 63 · 2025: 120 · 2024: 104 · 2023: 123 · 2022: 130 · 2021: 131 · 2020: 125 · 2019: 116 · 2018: 52 · 2017: 6
- FWCI
- 143.15
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- Proceedings - IEEE International Conference on Robotics and Automation/Proceedings (publishedVersion)
- arXiv (Cornell University) (publishedVersion)
- Warwick Research Archive Portal (University of Warwick) (submittedVersion)
- arXiv (Cornell University)
- Funders
- Engineering and Physical Sciences Research Council (EP/M019918/1)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Robotics and Sensor-Based Localization
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Vision and Imaging
- Physical Sciences › Earth and Planetary Sciences › Geology › 3D Surveying and Cultural Heritage
- Keywords
- monocular visual odometry, end-to-end learning, recurrent convolutional neural network, pose estimation, feature representation learning
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