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
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“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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“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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“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
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“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
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“In this section, we evaluate the proposed end-to-end monocular VO approach on the well-known KITTI VO/SLAM benchmark [3].”
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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
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“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
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“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
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Cited by
Sorted by citations
Items 901–969 of 969
Efficient Visual-Inertial Topological Mapping for Autonomous Robots Using EKF-Based Optimization
Gu, X., Cai, C., He, W., & Cheng, Y. (2025) · International Conference on Robotics, Control and Automation Engineering (RCAE) · doi:10.1109/rcae66389.2025.11355342
“Existing SLAM algorithms cover Visual SLAM (e.g., LSD-SLAM [3]), LiDAR-based SLAM (e.g., GMapping [4]), optimization-based SLAM, and deep learning-based SLAM (e.g., LSTM-based DeepVO [5], dynamic object-removing DS-SLAM [6].”
DeepWIO: A Lightweight Deep Wheel-Inertial Odometry for Mass-Produced Autonomous Driving Systems
Liu, S., Meng, W., & Zhang, X. (2025) · CAA International Conference on Vehicular Control and Intelligence (CVCI) · doi:10.1109/cvci66304.2025.11348631
“As a pioneering effort in this domain, DeepVO [12] employs a recurrent convolutional neural network to estimate poses from RGB image sequences.”
FVTVO: A FasterViT-Based Model for End-to-End Monocular Visual Odometry
Chen, M., Peng, D., Zhang, Z., Wen, Z., Onoda, H., & Liu, J. (2025) · International Symposium on Wireless Personal Multimedia Communications (WPMC) · doi:10.1109/wpmc67460.2025.11351117
“Table I presents the quantitative results of FVTVO and several representative baseline methods, including the traditional geometry-based ORB-SLAM3 [9], the learning-based models DeepVO [4], and multiple variants of TSformer-VO.”
An Optical Flow and Deep Learning Based Monocular Visual Odometry
Zhang, W., Li, Z., Peng, Y., Huang, H., & Qi, J. (2026) · Lecture notes in electrical engineering · doi:10.1007/978-981-95-5021-0_42
Dual Attention XYDeblur: Attention-Guided Divide-and-Conquer Strategy for Image Deblurring
Shin, J. S., & Lee, H. S. (2026) · IEEE Access · doi:10.1109/access.2026.3660952
“…of this manuscript and approving it for publication was Patrizia Livreri . as a crucial preprocessing step for various computer vision applications, including object detection [1], [2], semantic segmentation [3], [4], visual odometry [5], [6], and planning-oriented autonomous driving [7], [8].”
Analogy-Augmented Uncertainty-Aware Monocular Visual Odometry
Li, J., Sun, S., Xue, T., Liu, X., Zhang, J., Dong, H., & Lu, G. (2026) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2026.3664426
“Model architectures primarily include RNNs [11], CNNs [9], and RCNNs [12], [13].”
On the Real-World Deployment of Visual SLAM: Challenges, Solutions, and Cost Analysis
Mirmohammadsadeghi, S. P., Nikan, S., & Shami, A. A. (2026) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2026.3659036
“DeepVO [18] was the first end-to-end visual odometry using Region-based Convolutional Neural Networks (RCNN) and Long Short-Term Memory (LSTM), DeepTAM [19] introduces dense mapping and UndeepVO [20] recovers camera pose and depth maps in an unsupervised fashion.”
Gu, F., Ji, K., Dou, J., Zhao, H., Li, Y., & Long, X. (2026) · IEEE Sensors Journal · doi:10.1109/jsen.2026.3666303
“DeepVO [24] is an early representative method that combines convolutional neural networks (CNNs) with long short-term memory (LSTM) units to regress relative camera poses from monocular image sequences.”
Dynamic Geometric Calibration on Visual Odometry Performance for Autonomous Drone Navigation
Nogueira, F. D. C., EUPHRÁSIO, F. P. A., Honório, D. D. D. C., Domiciano, M. A. P., Caldeira, A. D., & Shiguemori, E. H. (2025) · IEEE Latin American GRSS & ISPRS Remote Sensing Conference (LAGIRS) · doi:10.1109/lagirs68367.2025.11414767
“The contemporary literature on monocular visual odometry has been substantially expanded with the advent of deep learning-based techniques [14], [15], especially those employing Convolutional Neural Networks (CNNs) [16], [17] and transformer architectures [18].”
VISTA‐Campus Dataset: VersatIle Slam DaTAset With Multimodal Sensor for Campus Environments
Zhang, S., Zhang, Y., Wu, J., Tang, W., Zhang, J., Song, K., Fang, F., & An, S. (2026) · Journal of Field Robotics · doi:10.1002/rob.70196
“…systems (Qin et al. 2018; Kerbl et al. 2023; Campos et al. 2021; Forster et al. 2014), and camera‐LiDAR fusion approaches (Shan et al. 2021; Lin and Zhang 2022), as well as deep learning frameworks (Teed and Deng 2021; Wang et al. 2017; Yang et al. 2024) dedicated to raw data processing.”
Kang, C. H., & Kim, S. Y. (2026) · Journal of Institute of Control Robotics and Systems · doi:10.5302/j.icros.2026.25.8005
Tak, S., Cho, K., Kim, S., & Hyun, J. (2026) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2026.3676103
“DeepVO infers pose directly from monocular RGB images using a CNN/RNN architecture [11], and UnDeepVO extends this to stereo images using unsupervised learning [12].”
Integration of Inertial and Optical Navigation Systems with Spatial Stabilization of a Video Camera
Sokolov, S. V., Погорелов, В. А., & Shvidchenko, S. A. (2025) · Optoelectronics Instrumentation and Data Processing · doi:10.3103/s8756699025700888
Salehi, A. (2018) · theses.fr (ABES) · doi:10.70675/78c7864fz5568z47d0z92dez87c9d22c84c0
“[129] présentent un réseau composé d’un CNN qui extrait des features, et d’un réseau récurrent de type LSTM chargé de la regression de la pose.”
Interactive computer vision through the Web
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Smart Automation and Robotics in Healthcare: Transforming Hospital Material Handling Systems
Alomran, O., & Yang, H. (2025) · Internet of things · doi:10.1007/978-3-032-02709-2_13
Feng, R., Wang, N., & Zhang, Z. (2026) · SAE technical papers on CD-ROM/SAE technical paper series · doi:10.4271/2026-01-0010
Loukkal, A. (2021) · theses.fr (ABES) · doi:10.70675/ad721e60z1d82z43ddza56fz45c72f787af1
Ego-rotation estimation based on learning
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Transformer-Based Visual Odometry and DepthEstimation for Wireless Capsule Endoscopy
Nazifi, N. (2025) · theses.fr (ABES) · doi:10.70675/b34a4ed6zd365z4120z9819zc848c48ba974
Yuan, Z., Shen, Y., Wang, J., & Wang, Y. (2026) · Research Square · doi:10.21203/rs.3.rs-9365602/v1
Self-Supervised Monocular Depth Estimation With Multimodal Fusion and Pose Enhancement
Chen, H., Wang, Y., Jia, S., Huang, S., & Zhang, J. (2026) · IEEE Sensors Journal · doi:10.1109/jsen.2026.3684347
Liang, Y., Zeng, H., Yi, F., Ren, Y., & Jiao, X. (2026) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2026.3687358
“DeepVO [9] utilizes LSTM networks for temporal modeling, enhancing the algorithm’s accuracy.”
Monocular Visual Odometry with Continuous 3D Landmark RefinementPreprint
Alizadeh, M., Goli, A., & Harati, A. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.6668908
Relative Pose Estimation Using Optical Flow-Guided Hybrid Supervision for Visual Odometry
Chen, X., Cao, Y., & Yu, H. (2026) · IEEE Access · doi:10.1109/access.2026.3688850
“For visual odometry, DeepVO [19] further formulated monocular VO as a sequence regression problem, using convolutional and recurrent networks to directly predict relative poses between consecutive frames.”
Liu, J., Xiao, Z., & Yan, B. (2026) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2026.3688325
“…VOs leverage multi-dimensional constraints, including inter-frame geometric consistency [9], [13], sequences contextual spatial-temporal correlations [9], [14], cross views prior knowledge representation [15], [16], semantic segmentation features [17], and optical flow consistency [11], [18], [19].”
InfluentialMonocular Visual Odometry with Continuous 3D Landmark RefinementPreprint
Alizadeh, M., Goli, A., & Harati, A. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.6727704
Jia, L., Zhang, Z., Qi, Y., & Song, P. (2026) · Measurement · doi:10.1016/j.measurement.2026.121787
Visual–Language Model–Driven Annotation and Analysis of Maneuver Data for Enhanced Vehicle Safety
Bai, L., 原崇裕, Osman, I. I., Lin, Z., Mirab, G., Saheb, A., Parnian, N., Shapiro, E., Shehata, M. S., & Liu, Z. (2026) · SAE International Journal of Transportation Safety · doi:10.4271/09-14-01-0032
SED-SLAM: Enhancing Monocular SLAM Under Image Distortions via Spatially Equalized Deep Feature
Zhu, L., Yan, L., Xie, H., Yang, X., Yang, X., & Li, A. (2026) · Lecture notes in computer science · doi:10.1007/978-981-95-7084-3_35
Liu, J., Song, R., Xiao, Z., & Yan, B. (2026) · Chinese Journal of Mechanical Engineering · doi:10.1016/j.cjme.2026.100321
Wei, Y., Wang, Y., Liu, S., Zhang, N. L., Yan, L., & Sheng, J. (2026) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2026.2663071
SCARF-SLAM: Spatially Confidence-aware Aggregation for Federated Implicit Neural SLAM
Rao, G., Chen, M., Hu, M., Zhang, S., & Liu, K. (2026) · IEEE International Conference on Mobility, Operations, Services and Technologies (MOST) · doi:10.1109/most69733.2026.00017
“Early works like PoseNet [30] and DeepVO [31] use convolutional neural networks (CNNs) or recurrent neural networks (RNNs) to predict the camera poses in end-to-end regression, but they lack of geometric constrains and generalization.”
Self-Supervised Monocular Visual Odometry Based on Multi-View Spatio-Temporal Feature Fusion
Liu, J., Xiao, Z., & Yan, B. (2026) · IEEE International Symposium on Circuits and Systems proceedings · doi:10.1109/iscas66217.2026.11561951
SmartFusion-SLAM: From Passive Fusion to Temporal Uncertainty-Aware Sensor Adaptation in BEV SLAM
Xia, Z., & Levorato, M. (2026) · ACM/IEEE International Conference on Cyber-Physical Systems (ICCPS) · doi:10.1109/iccps68698.2026.00007
“Learning-based methods employ deep neural networks to regress poses directly from raw multi-modal inputs [12], [13], [14], [15].”
MVOFormer: Flow-Semantic Transformer for Robust Monocular Visual Odometry
Li, J., Sun, S., Zhang, J., Liu, X., Hu, J. B., Lu, Z., Sajad Saeedi, G., & Lu, G. (2026) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2026.3707361
“Classical methods [1], [2], [3] rely on hand-crafted multi-stage pipelines (feature extraction, tracking, outlier rejection, and bundle adjustment), which are brittle under illumination changes, low texture, or dynamic objects and require careful parameter tuning.”
UniFormer-Based Dual Quaternion Network for Visual Odometry
Feng, Y., Xu, C., Yao, C., Si, B., Gu, C., & Yang, D. (2026) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2026.3707344
Multimodal Collaborative Object Localization
Wang, B. (2026) · Advances in computer vision and pattern recognition · doi:10.1007/978-981-92-0052-8_7
Zhang, B., Li, C. G., Wang, Z., Zhou, D., Lv, M., & Zhang, R. (2026) · IEEE Transactions on Aerospace and Electronic Systems · doi:10.1109/taes.2026.3710658
“DeepVO [22] pioneered this direction with recurrent convolutional neural networks capable of learning geometric features and temporal dependencies, thereby improving pose estimation accuracy.”
Wang, L., Sheng, K., He, Z., Li, J., Qin, Y., Dai, H., Wang, X. F., Yang, J., Yan, Q., Liu, C., et al. (2026) · IEEE Transactions on Automation Science and Engineering · doi:10.1109/tase.2026.3711284
“Learning-based approaches such as DeepVO [78] and DROID-SLAM [79] further enhanced performance and generalization by incorporating neural network priors.”
DriveSAM: Accurate and Robust Structure and Motion for Driving Scenarios
Wu, S., & Liu, J. (2026) · Lecture notes in computer science · doi:10.1007/978-981-92-3507-0_44
GeM-VO: Deep Stereo Odometry With Geometry Masks in Dynamic Environments
Xie, C., Yao, H., Xing, R., Hao, N., & He, F. (2026) · IEEE Sensors Journal · doi:10.1109/jsen.2026.3712373
“Training Datasets: Like most previous works [6], [9], [13], we train the model on KITTI-Odometry dataset.”
InfluentialDSCA-SLAM: Robust Dynamic Visual SLAM with YOLOv11 and Attention Mechanism
Duan, Z., Hui, F., Guo, G., Li, M., Peng, K., & Zhang, H. (2026) · Communications in computer and information science · doi:10.1007/978-981-92-3538-4_16
Adaptive Hybrid Patch Sampling for Deep Patch Visual Odometry
Lv, G., Li, Z., Shu, S., Zongyuan, L., Wang, W., Zhang, R., & Zhao, L. (2026) · International Conference on Artificial Intelligence, Big Data and Algorithms (CAIBDA) · doi:10.1109/caibda70336.2026.11621272
Is Physical Depth Sensor Necessary for Visual Odometry? Geometry-Aware Fine-Tuning
Yang, S., Wang, Y., & Zhang, J. (2026) · Youth Academic Annual Conference of Chinese Association of Automation (YAC) · doi:10.1109/yac71005.2026.11615440
“Early architectures, such as DeepVO [5], utilized Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) to implicitly model motion dynamics directly from raw optical sequences.”
Liu, J., Cao, W., Wu, Z., Chen, H., Li, Y., Wang, H., & Miao, Z. (2026) · International Conference on Control and Automation · doi:10.1109/icca69928.2026.11617993
“DeepVO [22] estimates visual odometry from monocular image sequences using convolutional and recurrent networks, while VINet [23] further incorporates inertial measurements to improve robustness under rapid motion.”
Li, X., Danapalasingam, K. A., Wahab, N. A., & Ngo, T. D. (2026) · Unconventional Resources · doi:10.1016/j.uncres.2026.100525
Kalman-UNet: Video Regression as Latent State Estimation With Neural Prediction-Correction
Li, W., Wang, L., & Lin, Z. (2026) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2026.3723739
He, J., & Liu, W. (2026) · Informatics · doi:10.3390/informatics13080132
“DeepVO combines convolutional feature extraction with recurrent modeling to estimate visual odometry from monocular image sequences [27].”
Frequency-Decoupled Event-Based Visual Odometry
Xu, T., Wang, L., Zhu, D., & Li, J. (2026) · Lecture notes in electrical engineering · doi:10.1007/978-981-95-6825-3_3
Peou, T., Srang, S., & Keo, L. (2026) · E3S Web of Conferences · doi:10.1051/e3sconf/202673601002
“DeepVO [8] demonstrated that a Recurrent Neural Network (RNN) can learn to regress pose directly from sequential image data.”
Guerazem, S., Rondao, D., & Aouf, N. (2026) · IEEE Transactions on Aerospace and Electronic Systems · doi:10.1109/taes.2026.3731320
A Sensor-Centric Survey of SLAM and Odometry for GPS-Denied EnvironmentsPreprint
Geladaris, A., & Polygerinos, P. (2026) · Research Square · doi:10.21203/rs.3.rs-10409860/v1
GCAFormer-VO: A Geometry and Correspondence-Aware Transformer for Visual Odometry
Bao, J., Wu, Q., Huang, J., & Yuan, X. (2026) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2026.3735107
“…state-of-the-art learning-based VO methods, including SWFormer-VO [27], TSFormer-VO [14], STDN-VO [15], Lin et al. [34] (IEEE TIM’25), TartanVO [32], SC-SfMLearner [33], Lin et al. [35] (IEEE Sensors J.’26), and DeepVO [6], in terms of translational and rotational errors on the KITTI dataset.”
HDVO: End-to-End Hierarchical Multimodal Visual Odometry with Depth-Guided Fusion
Chen, M., Tang, J., Onoda, H., & Liu, J. (2026) · IEEE Region 10 Symposium (TENSYMP) · doi:10.1109/tensymp68909.2026.11704010
“DeepVO [1] is one of the representative early works in this direction, which combines convolutional neural networks and recurrent neural networks to jointly model spatial appearance and temporal motion information.”
LOL-VO: Toward Robust Visual Perception and Ego-Motion Estimation in Low-Light Conditions
Xie, C., Hao, N., Xing, R., & He, F. (2026) · IEEE Transactions on Automation Science and Engineering · doi:10.1109/tase.2026.3738192
“DeepVO [8] pioneered the use of Recurrent Convolutional Neural Networks (R-CNNs) for end-to-end pose regression, directly estimating camera motion from sequential RGB images without relying on traditional geometric modules.”
DeepEVO: Towards Robust Visual Odometry via Attention-Based Events and Video Fusion
Guo, X., Du, B., Wen, H., & Shen, Y. (2026) · Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering · doi:10.1007/978-3-032-33662-0_36
Self-citationDINO-VO: Learning Where to Focus for Enhanced State Estimation
Chen, Q., Li, G., Hu, S., Gao, X., Ma, J., Xue, X., & Pu, J. Y. (2026) · doi:10.1109/cvprf71909.2026.00154
“Towards end-to-end SLAM systems, DeepVO [62] and UnDeepVO [36] presented a novel end-to-end framework for monocular VO by using deep Neural Networks.”
Bronchoscope Tip Localization With Learned Airway Constraints and an Open Source Simulator
Nicotra, E., Davies, J., Sharma, B., Ji, A., Zhu, K., Phan, P. T., Lovell, N. H., & Do, T. N. (2026) · Advanced Intelligent Systems · doi:10.1002/aisy.70575
RetiGlue-SLAM: A Robust Visual SLAM System Designed for Low-Light and Complex Environments
Li, Y., Liang, X., Guo, G., Su, P., & An, Q. (2026) · ISPRS International Journal of Geo-Information · doi:10.3390/ijgi15100468
ATM-SLAM: Adaptive Temporal Memory Visual SLAM in Severe Rainy Conditions
Chen, B., Wang, W., Wei, L., Wan, Z., Zhou, C., & Zhu, T. (2026) · ACM Annual International Conference on Mobile Computing and Networking (MobiCom) · doi:10.1145/3795866.3796699
Transformer-Based Model for Monocular Visual Odometry: A Video Understanding Approach
Françani, A. O., & Maximo, M. R. O. A. (2023) · arXiv · doi:10.48550/arxiv.2305.06121 · Cited by 38
“Among the approaches to address the monocular VO, there are traditional geometry-based methods [2], [3], deep learning-based methods that use end-to-end architectures [4], [5], and hybrid methods with deep learning in certain modules of the geometry-based methods [6], [7], [8].”
Influential
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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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