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Approaches, Challenges, and Applications for Deep Visual Odometry: Toward Complicated and Emerging Areas
Open accessAbstract
Visual odometry (VO) is a prevalent way to deal with the relative localization problem, which is becoming increasingly mature and accurate, but it tends to be fragile under challenging environments. Comparing with classical geometry-based methods, deep-learning-based methods can automatically learn effective and robust representations, such as depth, optical flow, feature, ego-motion, etc., from data without explicit computation. Nevertheless, there still lacks a thorough review of the recent advances of deep-learning-based VO (Deep VO). Therefore, this article aims to gain a deep insight on how deep learning can profit and optimize the VO systems. We first screen out a number of qualifications, including accuracy, efficiency, scalability, dynamicity, practicability, and extensibility, and employ them as the criteria. Then, using the offered criteria as the uniform measurements, we detailedly evaluate and discuss how deep learning improves the performance of VO from the aspects of depth estimation, feature extraction and matching, and pose estimation. We also summarize the complicated and emerging areas of Deep VO, such as mobile robots, medical robots, augmented and virtual reality, etc. Through the literature decomposition, analysis, and comparison, we finally put forward a number of open issues and raise some future research directions in this field.
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Cites
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 1×
“For the former, we use RMSE (Root Mean Square Error) [23] to measure the bias of ground truth and estimation, which directly describes algorithm accuracy.”
MethodORB-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 2×
“use root-mean-square error (RMSE, defined in [28]) or average trajectory error (ATE, defined in [4]) to measure the bias of ground truth and estimation, which directly describes algorithm accuracy.”
BackgroundMethodLSD-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
Past, Present, and Future of Simultaneous Localization and Mapping: Toward the Robust-Perception Age
Cadena, C., Carlone, L., Carrillo, H., Latif, Y., Scaramuzza, D., Neira, J., Reid, I., & Leonard, J. J. (2016) · IEEE Transactions on Robotics · doi:10.1109/tro.2016.2624754 · Cited by 3,596 · cited 1×
“Most recent review papers focus on geometrybased methods, less on deep-learning-based approaches [16], [18], [19].”
BackgroundDepth Map Prediction from a Single Image using a Multi-Scale Deep Network
Eigen, D., Puhrsch, C., & Fergus, R. (2014) · arXiv · doi:10.48550/arxiv.1406.2283 · Cited by 3,442 · cited 1×
“1) Supervised Learning Methods: For supervised learning, the use of supervised-based methods was first proposed in [29].”
MethodUnsupervised Monocular Depth Estimation with Left-Right Consistency
Godard, C., Aodha, O. M., & Brostow, G. J. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.699 · Cited by 3,270 · cited 2×
“[46] introduced a method that can simultaneously infer disparities that warp the left images to match the right one or warp the right image to match the left one, thereby obtaining better depth estimation by enforcing left–right depth consistency.”
BackgroundMethodEngel, 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
Unsupervised Learning of Depth and Ego-Motion from Video
Zhou, T., Brown, M., Snavely, N., & Lowe, D. G. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.700 · Cited by 2,910 · cited 1×
“[47] first synthesized a new image of the scene from different views of the input image.”
BackgroundPoseNet: 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 · cited 1×
“1) Supervised Methods: PoseNet [83] was the work that the first to utilize CNN to regress 6 DOF camera pose from the monocular image in an end-to-end manner without graph optimization.”
MethodUnsupervised CNN for Single View Depth Estimation: Geometry to the Rescue
Garg, R., B.G., V. K., Carneiro, G., & Reid, I. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-46484-8_45 · Cited by 1,632 · cited 1×
“The method using unsupervised learning was first introduced in [45], which estimates the depth information from single-view images.”
MethodLIFT: Learned Invariant Feature Transform
Yi, K. M., Trulls, E., Lepetit, V., & Fua, P. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-46466-4_28 · Cited by 1,306 · cited 1×
“[70] proposed a unified framework for local feature detection and descriptors, and Zeng et al.”
BackgroundGeoNet: Unsupervised Learning of Dense Depth, Optical Flow and Camera Pose
Yin, Z., & Shi, J. (2018) · 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.2018.00212 · Cited by 1,280
Learning Depth from Single Monocular Images Using Deep Convolutional Neural Fields
Liu, F., Shen, C., Lin, G., & Reid, I. D. (2015) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2015.2505283 · Cited by 1,231 · cited 1×
“Therefore, Liu et al. [31] proposed a continuous CRF and CNN based framework, which transforms the depth estimation into learning problems.”
Background3DMatch: Learning Local Geometric Descriptors from RGB-D Reconstructions
Zeng, A., Song, S., Nießner, M., Fisher, M., Xiao, J., & Funkhouser, T. A. (2017) · 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.2017.29 · Cited by 1,172 · cited 1×
“[71] used a 3-D convolutional layer-based framework to learn local geometric descriptors.”
MethodFrom Coarse to Fine: Robust Hierarchical Localization at Large Scale
Sarlin, P.-E., Cadena, C., Siegwart, R., & Dymczyk, M. (2019) · 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.2019.01300 · Cited by 1,142 · cited 1×
“In this application, the algorithm more focus on dynamic [98], illumination changing [112], long-term [113] and large-scale environments [114].”
BackgroundDeepvo: 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 · cited 3×
“However, now we only use some simple and general techniques, such as CNN, RNN, GAN, and its hybrids [24], [25], [38], [39], [85], [86].”
MethodVisual simultaneous localization and mapping: a survey
Fuentes-Pacheco, J., Ruiz-Ascencio, J., & Rendón-Mancha, J. M. (2015) · Artificial Intelligence Review · doi:10.1007/s10462-012-9365-8 · Cited by 883 · cited 1×
“Most recent review papers focus on geometrybased methods, less on deep-learning-based approaches [16], [18], [19].”
BackgroundGeometric Loss Functions for Camera Pose Regression with Deep Learning
Kendall, A., & Cipolla, R. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.694 · Cited by 803 · cited 1×
“Then, this work was extended in [84] by utilizing multiview geometry as a source of training data to improve the PoseNet performance.”
MethodUnsupervised Learning of Depth and Ego-Motion from Monocular Video Using 3D Geometric Constraints
Mahjourian, R., Wicke, M., & Angelova, A. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00594 · Cited by 775
DeMoN: Depth and Motion Network for Learning Monocular Stereo
Ummenhofer, B., Zhou, H., Uhrig, J., Mayer, N., Ilg, E., Dosovitskiy, A., & Brox, T. (2017) · 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.2017.596 · Cited by 712 · cited 2×
“based methods, most of them use depth and optical flow to assist the training process [96], [104]–[106].”
MethodZhan, 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
Sparse-to-Dense: Depth Prediction from Sparse Depth Samples and a Single Image
Ma, F., & Karaman, S. (2018) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2018.8460184 · Cited by 668
L2-Net: Deep Learning of Discriminative Patch Descriptor in Euclidean Space
Tian, Y., Fan, B., & Wu, F. (2017) · 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.2017.649 · Cited by 601 · cited 1×
“Progressive sampling strategies [73], [74] were used to allow the network to traverse”
MethodUnDeepVO: 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
Semi-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
Image-Based Localization Using LSTMs for Structured Feature Correlation
Walch, F., Hazirbas, C., Leal-Taixe, L., Sattler, T., Hilsenbeck, S., & Cremers, D. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.75 · Cited by 521 · cited 2×
“However, now we only use some simple and general techniques, such as CNN, RNN, GAN, and its hybrids [24], [25], [38], [39], [85], [86].”
MethodMulti-scale Continuous CRFs as Sequential Deep Networks for Monocular Depth Estimation
Xu, D., Ricci, E., Ouyang, W., Wang, X., & Sebe, N. (2017) · 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.2017.25 · Cited by 484
Cao, Y., Wu, Z., & Shen, C. (2017) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2017.2740321 · Cited by 467
D3VO: Deep Depth, Deep Pose and Deep Uncertainty for Monocular Visual Odometry
Yang, N., von Stumberg, L., Wang, R., & Cremers, D. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.00136 · Cited by 438 · cited 3×
“D3VO [108] incorporates depth estimation, pose estimation and pose uncertainty into a framework to improve the performance.”
BackgroundCodeSLAM - Learning a Compact, Optimisable Representation for Dense Visual SLAM
Bloesch, M., Czarnowski, J., Clark, R., Leutenegger, S., & Davison, A. J. (2018) · 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.2018.00271 · Cited by 401
Learning Compact Binary Descriptors with Unsupervised Deep Neural Networks
Lin, K., Lu, J., Chen, C., & Zhou, J. (2016) · 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.2016.133 · Cited by 368 · cited 1×
“To learn compact binary descriptors, an unsupervised-based method was introduced in [69], which enforces the criterion of minimal loss quantization, uniformly distributed code, and uncorrelated bits.”
MethodSelf-citationMultiple-Robot Simultaneous Localization and Mapping: A Review
Saeedi, S., Trentini, M., Seto, M., & Li, H. (2016) · Journal of Field Robotics · doi:10.1002/rob.21620 · Cited by 324 · cited 7×
“Some review papers focus on specific aspects, including multirobot SLAM [20], semantic information [23], and dynamic environment processing [22].”
BackgroundInfluentialKumar, B. G. V., Carneiro, G., & Reid, I. D. (2016) · 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.2016.581 · Cited by 308 · cited 2×
“arity Multiclass Classification Embedding or Fig. 4. Comparison of different types of input, network architectures, loss functions and output used to train the method of local image descriptor models [76]. From top to bottom: (a) Linear Transform G trained by the metric learning loss and produce a feature embedding. (…”
MethodReview of visual odometry: types, approaches, challenges, and applications
Aqel, M. O. A., Marhaban, M. H., Saripan, M. I., & Ismail, N. B. (2016) · SpringerPlus · doi:10.1186/s40064-016-3573-7 · Cited by 294 · cited 1×
“[17] was the only review that discussing VO separately.”
BackgroundOn the Uncertainty of Self-Supervised Monocular Depth Estimation
Poggi, M., Aleotti, F., Tosi, F., & Mattoccia, S. (2020) · 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/cvpr42600.2020.00329 · Cited by 291
Johnston, A., & Carneiro, G. (2020) · 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/cvpr42600.2020.00481 · Cited by 281
Deep Auxiliary Learning for Visual Localization and Odometry
Valada, A., Radwan, N., & Burgard, W. (2018) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2018.8462979 · Cited by 260
VidLoc: A Deep Spatio-Temporal Model for 6-DoF Video-Clip Relocalization
Clark, R., Wang, S., Markham, A., Trigoni, N., & Wen, H. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.284 · Cited by 249 · cited 2×
“A spatial–temporal model based on bidirectional RNN was proposed in [86] to learn the relationship between consecutive images.”
MethodSurvey of computer vision algorithms and applications for unmanned aerial vehicles
Al-Kaff, A., Martín, D., García, F., Escalera, A. D. L., & María Armingol, J. (2018) · Expert Systems with Applications · doi:10.1016/j.eswa.2017.09.033 · Cited by 246 · cited 1×
“Compared with autonomous vehicles, using VO into UAVs is more challenging due to the computational capability and 3-D maneuverability [113], [114].”
MethodEnd-to-end, sequence-to-sequence probabilistic visual odometry through deep neural networks
Wang, S., Clark, R., Wen, H., & Trigoni, N. (2017) · The International Journal of Robotics Research · doi:10.1177/0278364917734298 · Cited by 244 · cited 4×
“cific conditions, and have demonstrated the huge potential of integrated deep learning to solve the problems faced by geometry-based methods [24]–[27].”
BackgroundMethodInfluentialVLocNet++: Deep Multitask Learning for Semantic Visual Localization and Odometry
Radwan, N., Valada, A., & Burgard, W. (2018) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2018.2869640 · Cited by 237 · cited 1×
“VLocNet++ [102] embedded geometric and semantic knowledge of the scene into”
BackgroundExploring 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 2×
“[90] Supervised Mo Outdoor N M Y Y N Y N”
BackgroundQuad-Networks: Unsupervised Learning to Rank for Interest Point Detection
Savinov, N., Seki, A., Ladický, Ľ., Sattler, T., & Pollefeys, M. (2017) · 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.2017.418 · Cited by 191 · cited 1×
“Thus, Savinov et al. [74] introduced a method that can generate repeated interesting points even if the image undergoes a transformation.”
Towards Better Generalization: Joint Depth-Pose Learning Without PoseNet
Zhao, W., Liu, S., Shu, Y., & Liu, Y. (2020) · 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/cvpr42600.2020.00917 · Cited by 186 · cited 1×
“based methods, most of them use depth and optical flow to assist the training process [96], [104]–[106].”
MethodLearning Depth From Single Images With Deep Neural Network Embedding Focal Length
He, L., Wang, G., & Hu, Z. (2018) · IEEE Transactions on Image Processing · doi:10.1109/tip.2018.2832296 · Cited by 178 · cited 1×
“And the ambiguity between focal length and monocular depth was demonstrated in [36].”
BackgroundAlmalıoğlu, Y., Saputra, M. R. U., de Gusmao, P. P. B., Markham, A., & Trigoni, N. (2019) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2019.8793512 · Cited by 166 · cited 1×
“[61] used GANs for camera pose estimation from monocular image sequences.”
MethodPilzer, A., Lathuilière, S., Sebe, N., & Ricci, E. (2019) · 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.2019.01000 · Cited by 155 · cited 1×
“[60] Unsupervised Mo Outdoor N H Y A A Y N”
BackgroundYang, C., Maimone, M. W., & Matthies, L. H. (2006) · IEEE Robotics & Automation Magazine · doi:10.1109/mra.2006.1638016 · Cited by 150 · cited 1×
“Besides, VO is also widely used in underwater robots [119], [120], space exploration robots [121],”
BackgroundMonocular depth estimation with hierarchical fusion of dilated CNNs and soft-weighted-sum inference
Li, B., Dai, Y., & He, M. (2018) · Pattern Recognition · doi:10.1016/j.patcog.2018.05.029 · Cited by 142
Learning Spread-Out Local Feature Descriptors
Zhang, X., Yu, F. X., Kumar, S., & Chang, S. (2017) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.492 · Cited by 142 · cited 1×
“Then, this work was extended in [78] by proposing a regularization term to spread out the local feature descriptor in descriptor space, and the proposed regularization can improve all methods using pairwise or triplet loss.”
MethodDeep Monocular Depth Estimation via Integration of Global and Local Predictions
Kim, Y., Jung, H., Min, D., & Sohn, K. (2018) · IEEE Transactions on Image Processing · doi:10.1109/tip.2018.2836318 · Cited by 127 · cited 1×
“[54] Supervised Mo In/Outdoor N L A A A N N”
BackgroundHan, L., Lin, Y., Du, G., & Lian, S. (2019) · 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros40897.2019.8968467 · Cited by 125 · cited 1×
“erature focus on learning-based fusion methods [11], [135], [136].”
BackgroundMonocular Visual-Inertial State Estimation for Mobile Augmented Reality
Li, P.-L., Qin, T., Hu, B., Zhu, F., & Shen, S.-J. (2017) · International Symposium on Mixed and Augmented Reality · doi:10.1109/ismar.2017.18 · Cited by 123
ClusterVO: Clustering Moving Instances and Estimating Visual Odometry for Self and Surroundings
Huang, J., Yang, S. Y., Mu, T., & Hu, S. (2020) · 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/cvpr42600.2020.00224 · Cited by 119
Generative Adversarial Networks for Unsupervised Monocular Depth Prediction
Aleotti, F., Tosi, F., Poggi, M., & Mattoccia, S. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-11009-3_20 · Cited by 117 · cited 1×
“MonoGAN [44] first applied GAN to monocular depth estimation.”
MethodAbolDeepIO: A Novel Deep Inertial Odometry Network for Autonomous Vehicles
Esfahani, M. A., Wang, H., Wu, K., & Yuan, S. (2019) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2019.2909064 · Cited by 114 · cited 2×
“Superior performance on accurate and effective are demonstrated in both featurebased methods [4]–[7] and direct methods [8]–[10], which pushes the classical geometry-based VO toward real-world applications [11]–[15].”
BackgroundDepthNet: A Recurrent Neural Network Architecture for Monocular Depth Prediction
Kumar, A. C. S., Bhandarkar, S. M., & Prasad, M. (2018) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw.2018.00066 · Cited by 112 · cited 2×
“Moreover, an LSTM-based architecture was also proposed to explore whether RCNN can learn accurate spatial–temporal depth estimation without interframe geometric consistency or pose supervision [39].”
MethodCvisic, I., Cesic, J., Marković, I., & Petrović, I. (2018) · J. Field Robotics · doi:10.1002/rob.21762 · Cited by 112 · cited 1×
“Recently, there has shown a growing interest in fusing multiple cameras [117], [118].”
BackgroundBeyond Tracking: Selecting Memory and Refining Poses for Deep Visual Odometry
Xue, F., Wang, X., Li, S., Wang, Q., Wang, J., & Zha, H. (2019) · 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.2019.00877 · Cited by 109 · cited 1×
“In [137], a spatial–temporal attention mechanism is applied to conduct the contexts for feature selection.”
MethodSimultaneous Stereoscope Localization and Soft-Tissue Mapping for Minimal Invasive Surgery
Mountney, P., Stoyanov, D. V., Davison, A. J., & Yang, G. (2006) · Lecture notes in computer science · doi:10.1007/11866565_43 · Cited by 107 · cited 1×
“Besides, in surgical applications, VO also gained a big attention, such as surgical systems, surgical assistance robot, and image-guided surgery systems [128], [129].”
BackgroundNormal Assisted Stereo Depth Estimation
Kusupati, U., Cheng, S., Chen, R., & Su, H. (2020) · 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/cvpr42600.2020.00226 · Cited by 103
Bilateral Cyclic Constraint and Adaptive Regularization for Unsupervised Monocular Depth Prediction
Wong, A., & Soatto, S. (2019) · 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.2019.00579 · Cited by 97 · cited 1×
“To enforce consistency be-tween the left and right disparities, bilateral cyclic consistency constraints were used in [50].”
MethodBeyond Photometric Loss for Self-Supervised Ego-Motion Estimation
Shen, T., Luo, Z., Zhou, L., Deng, H., Zhang, R., Fang, T., & Quan, L. (2019) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2019.8793479 · Cited by 91 · cited 1×
“[101] proposed to incorporate intermediate geometric information such as pairwise matching to the pose esti-”
BackgroundLi, R., SQUYRES, S. W., Arvidson, R. E., Archinal, B. A., Bell, J. F., Cheng, Y., Crumpler, L. S., Marais, D. J. D., Di, K., Ely, T., et al. (2005) · Photogrammetric Engineering & Remote Sensing · doi:10.14358/pers.71.10.1129 · Cited by 90
Multi-camera visual SLAM for autonomous navigation of micro aerial vehicles
Yang, S., Scherer, S., Yi, X., & Zell, A. (2017) · Robotics Auton. Syst. · doi:10.1016/j.robot.2017.03.018 · Cited by 88 · cited 1×
“Recently, there has shown a growing interest in fusing multiple cameras [117], [118].”
BackgroundThe adaptability and challenges of autonomous vehicles to pedestrians in urban China
Wang, K., Li, G., Chen, J., Yan, L., Chen, T., Chen, L., & Xia, Q. (2020) · Accident Analysis & Prevention · doi:10.1016/j.aap.2020.105692 · Cited by 87 · cited 1×
“In mobile robotic systems, the autonomous vehicle is a rapidly advancing application area of VO with unstructured environments [107]–[109].”
BackgroundSelf-citationPatchBatch: A Batch Augmented Loss for Optical Flow
Gadot, D., & Wolf, L. (2016) · 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.2016.459 · Cited by 86 · cited 1×
“Gadot et al. [77] used Siamese CNN to learn the descriptors of both images, and then compared the learned descriptors using the L2 norm.”
MethodUnsupervised Deep Visual-Inertial Odometry with Online Error Correction for RGB-D Imagery
Shamwell, E. J., Lindgren, K., Leung, S., & Nothwang, W. D. (2020) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2019.2909895 · Cited by 86 · cited 1×
“erature focus on learning-based fusion methods [11], [135], [136].”
BackgroundSimultaneous Localization and Mapping in the Epoch of Semantics: A Survey
Sualeh, M., & Kim, G.-W. (2018) · International Journal of Control Automation and Systems · doi:10.1007/s12555-018-0130-x · Cited by 85 · cited 1×
“Some review papers focus on specific aspects, including multirobot SLAM [20], semantic information [23], and dynamic environment processing [22].”
BackgroundOngoing Evolution of Visual SLAM from Geometry to Deep Learning: Challenges and Opportunities
Li, R., Wang, S., & Gu, D. (2018) · Cognitive Computation · doi:10.1007/s12559-018-9591-8 · Cited by 80 · cited 1×
“[21] provided a review that focuses the ongoing evolution of visual SLAM from model-based methods to learning-based methods.”
BackgroundSelf-Supervised Deep Visual Odometry With Online Adaptation
Li, S., Wang, X., Cao, Y., Xue, F., Yan, Z., & Zha, H. (2020) · 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/cvpr42600.2020.00637 · Cited by 78 · cited 1×
“[103] Unsupervised Mo In/Outdoor N H Y Y N Y Y”
BackgroundGeometric Correspondence Network for Camera Motion Estimation
Tang, J., Folkesson, J. B., & Jensfelt, P. (2018) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2018.2794624 · Cited by 76 · cited 1×
“Although this method cannot achieve the performance of traditional methods, it can generate confidence for specific Further, a CNN-and RNN-based framework was proposed in [80].”
MethodCognitive Navigation by Neuro-Inspired Localization, Mapping, and Episodic Memory
Tang, H., Yan, R., & Tan, K. C. (2017) · IEEE Transactions on Cognitive and Developmental Systems · doi:10.1109/tcds.2017.2776965 · Cited by 75
Learning to Detect Features in Texture Images
Zhang, L., & Rusinkiewicz, S. M. (2018) · 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.2018.00662 · Cited by 75 · cited 1×
“Therefore, a loss function was developed in [77] that can maximize the peak value of the”
BackgroundMonocular Depth Prediction Using Generative Adversarial Networks
Kumar, A. C. S., Bhandarkar, S. M., & Prasad, M. (2018) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw.2018.00068 · Cited by 72 · cited 1×
“Compared with MonoGAN, the generator in [49] consists of DepthNet and PoseNet, which are used for depth map estimation and camera pose estimation, respectively, and uses the estimated depth map and camera pose parameters to generate image pairs.”
MethodA critique of current developments in simultaneous localization and mapping
Huang, S., & Dissanayake, G. (2016) · International Journal of Advanced Robotic Systems · doi:10.1177/1729881416669482 · Cited by 71 · cited 1×
“Most recent review papers focus on geometrybased methods, less on deep-learning-based approaches [16], [18], [19].”
BackgroundProgressive Hard-Mining Network for Monocular Depth Estimation
Zhang, Z., Xu, C., Yang, J. J., Gao, J., & Cui, Z. (2018) · IEEE Transactions on Image Processing · doi:10.1109/tip.2018.2821979 · Cited by 64 · cited 1×
“Additionally, a novel progressive hard-mining network was proposed in [43] to solve the problem of the large semantic gap and accumulated error over scales.”
BackgroundReal-Time Visual–Inertial SLAM Based on Adaptive Keyframe Selection for Mobile AR Applications
Piao, J.-C., & Kim, S.-D. (2019) · IEEE transactions on multimedia · doi:10.1109/tmm.2019.2913324 · Cited by 60
Deep hierarchical guidance and regularization learning for end-to-end depth estimation
Zhang, Z., Xu, C., Yang, J. J., Tai, Y. Y., & Chen, L. (2018) · Pattern Recognition · doi:10.1016/j.patcog.2018.05.016 · Cited by 58 · cited 1×
“Since then, a lot of work has utilized the multiscale features of the network and depth structure information to improve the performance of depth estimation [30]–[35].”
BackgroundBarnes, D., Maddern, W., Pascoe, G., & Posner, I. (2018) · 2018 IEEE International Conference on Robotics and Automation (ICRA) · doi:10.1109/icra.2018.8460564 · Cited by 57
DPC-Net: Deep Pose Correction for Visual Localization
Peretroukhin, V., & Kelly, J. W. (2017) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2017.2778765 · Cited by 56
Learning Deep Descriptors with Scale-Aware Triplet Networks
Keller, M., Chen, Z., Maffra, F., Schmuck, P., & Chli, M. (2018) · 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.2018.00292 · Cited by 53 · cited 1×
“To learn consistent descriptors, a novel mixed-context loss and scale-aware sampling method were proposed in [68].”
MethodHierarchical Metric Learning and Matching for 2D and 3D Geometric Correspondences
Fathy, M. E., Tran, Q.-H., Zia, M. Z., Vernaza, P., & Chandraker, M. K. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01267-0_49 · Cited by 52
Multitask Learning for Object Localization With Deep Reinforcement Learning
Wang, Y., Zhang, L., Wang, L., & Wang, Z. (2018) · IEEE Transactions on Cognitive and Developmental Systems · doi:10.1109/tcds.2018.2885813 · Cited by 52 · cited 1×
“Superior performance on accurate and effective are demonstrated in both featurebased methods [4]–[7] and direct methods [8]–[10], which pushes the classical geometry-based VO toward real-world applications [11]–[15].”
BackgroundCost-effective visual odometry system for vehicle motion control in agricultural environments
Zaman, S., Comba, L., Biglia, A., Aimonino, D. R., Barge, P., & Gay, P. (2019) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2019.03.037 · Cited by 50 · cited 1×
“Besides, visual odometry is also widely used in underwater robots [122]–[127], space exploration robots [128]–[131] and agriculture robots [132], etc.”
Geometric Consistency for Self-Supervised End-to-End Visual Odometry
Iyer, G., Jatavallabhula, K. M., Gupta, G., Krishna, K. M., & Paull, L. (2018) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw.2018.00064 · Cited by 49
Selective visual odometry for accurate AUV localization
Bellavia, F., Fanfani, M., & Colombo, C. (2015) · Autonomous Robots · doi:10.1007/s10514-015-9541-1 · Cited by 44 · cited 1×
“Besides, VO is also widely used in underwater robots [119], [120], space exploration robots [121],”
BackgroundDynamic Attention-based Visual Odometry
Kuo, X.-Y., Liu, C., Lin, K.-C., & Lee, C. (2020) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw50498.2020.00026 · Cited by 43
Kernelized Subspace Pooling for Deep Local Descriptors
Wei, X., Zhang, Y., Gong, Y., & Zheng, N. (2018) · 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.2018.00200 · Cited by 42 · cited 1×
“tion, a subspace pooling [75] was proposed instead of the max pooling or average pooling.”
BackgroundSLAM algorithm applied to robotics assistance for navigation in unknown environments
Cheein, F. A., López, N. M., Soria, C. M., di Sciascio, F. A., Перейра, Ф. Л., & Carelli, R. (2010) · Journal of NeuroEngineering and Rehabilitation · doi:10.1186/1743-0003-7-10 · Cited by 40 · cited 1×
“Besides, in surgical applications, VO also gained a big attention, such as surgical systems, surgical assistance robot, and image-guided surgery systems [128], [129].”
BackgroundUnDEMoN: Unsupervised Deep Network for Depth and Ego-Motion Estimation
Babu, V. M., Das, K., Majumdar, A., & Kumar, S. (2018) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2018.8593864 · Cited by 39
A precise visual localisation method for the Chinese Chang’e‐4 Yutu‐2 rover
Ma, Y., Liu, S., Sima, B., Wen, B., Song, P., & Jia, Y. (2020) · The Photogrammetric Record · doi:10.1111/phor.12309 · Cited by 34 · cited 1×
“autonomous and evolutionary robots, medical robots, augmented/mixed/virtual reality, and other complicated and emerging applications based on localization information, such as indoor and outdoor navigation, scene understanding, and space exploration [1]–[3].”
BackgroundSfMLearner++: Learning Monocular Depth & Ego-Motion Using Meaningful Geometric Constraints
Prasad, V., & Bhowmick, B. (2019) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv.2019.00226 · Cited by 30
Jacobson, A., Zeng, F., Smith, D., Boswell, N., Peynot, T., & Milford, M. J. (2018) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2018.8593750 · Cited by 30 · cited 1×
“In this application, the algorithm more focus on dynamic [98], illumination changing [110], long-term [111], and large-scale environments [112].”
BackgroundMulti-sensor based real-time 6-DoF pose tracking for wearable augmented reality
Fang, W., Zheng, L., & Wu, X. (2017) · Computers in Industry · doi:10.1016/j.compind.2017.06.002 · Cited by 27 · cited 1×
“In mobile devices, to improve accuracy and obtain absolute scale, the devices fuse data from other sensors such as IMU [148]–[151].”
MethodA feature-supervised generative adversarial network for environmental monitoring during hazy days
Wang, K., Zhang, S., Chen, J., Ren, F., & Xiao, L. (2020) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2020.141445 · Cited by 27 · cited 1×
“Superior performance on accurate and effective are demonstrated in both featurebased methods [4]–[7] and direct methods [8]–[10], which pushes the classical geometry-based VO toward real-world applications [11]–[15].”
BackgroundSelf-citationLearning Kalman Network: A deep monocular visual odometry for on-road driving
Zhao, C., Sun, L., Yan, Z., Neumann, G. M., Duckett, T., & Stolkin, R. (2019) · Robotics and Autonomous Systems · doi:10.1016/j.robot.2019.07.004 · Cited by 26 · cited 1×
“In mobile robotic systems, the autonomous vehicle is a rapidly advancing application area of visual odometry with unstructured environments [109]–[111].”
BackgroundAdversarial Learning for Joint Optimization of Depth and Ego-Motion
Wang, A., Fang, Z., Gao, Y., Tan, S., Wang, S., Ma, S., & Hwang, J. (2020) · IEEE Transactions on Image Processing · doi:10.1109/tip.2020.2968751 · Cited by 25 · cited 1×
“[63] Unsupervised Mo In/Outdoor N H A A A A N”
BackgroundSelf-citationDepth Estimation of Video Sequences With Perceptual Losses
Wang, A., Fang, Z., Gao, Y., Jiang, X., & Ma, S. (2018) · IEEE Access · doi:10.1109/access.2018.2846546 · Cited by 24 · cited 1×
“[48] utilized the camera parameters to synthesize the original view in a”
MethodSelf-citationMonocular depth estimation with guidance of surface normal map
Han, Y., Zhang, S., Zhang, Y., & Zhang, L. (2017) · Neurocomputing · doi:10.1016/j.neucom.2017.08.074 · Cited by 24 · cited 1×
“To alleviate the depth ambiguity problem, invariant surface normal was used to assist depth estimation [40].”
MethodUsing Unsupervised Deep Learning Technique for Monocular Visual Odometry
Liu, Q., Li, R., Hu, H., & Gu, D. (2019) · IEEE Access · doi:10.1109/access.2019.2896988 · Cited by 23 · cited 1×
“Besides, different from using stereo images to obtain absolute scale estimation, using depth information obtained from 3-D LIDARs to train the network to obtain absolute scale estimation was introduced in [100].”
MethodBazrafkan, S., Javidnia, H., Lemley, J., & Corcoran, P. (2018) · Journal of Electronic Imaging · doi:10.1117/1.jei.27.4.043041 · Cited by 23 · cited 1×
“Since then, a lot of work has utilized the multi-scale features of the network and depth structure information to improve the performance of depth estimation [25]–[30].”
BackgroundZhou, L., & Kaess, M. (2020) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2020.2976301 · Cited by 21 · cited 1×
“[65] Unsupervised Mo Outdoor N H Y A Y Y N”
BackgroundWang, K., Huang, Z. F., & Zhong, Z. (2014) · Chinese Journal of Mechanical Engineering · doi:10.3901/cjme.2014.0707.118 · Cited by 20
Self-citationDepth estimation from single monocular images using deep hybrid network
Grigorev, A., Jiang, F., Rho, S., Jifara, W., Liu, S., & Sai, S. (2016) · Multimedia Tools and Applications · doi:10.1007/s11042-016-4200-x · Cited by 19 · cited 2×
“However, now we only use some simple and general techniques, such as CNN, RNN, GAN, and its hybrids [24], [25], [38], [39], [85], [86].”
MethodVisual Localization of Wireless Capsule Endoscopes Aided by Artificial Neural Networks
Dimas, G., Iakovidis, D. K., Ciuti, G., Karargyris, A., & Koulaouzidis, A. (2017) · Proceedings - IEEE Symposium on Computer-Based Medical Systems · doi:10.1109/cbms.2017.67 · Cited by 18
Ci, W., Huang, Y., & Hu, X. (2019) · IEEE Sensors Journal · doi:10.1109/jsen.2019.2917936 · Cited by 18 · cited 1×
“In mobile robotic systems, the autonomous vehicle is a rapidly advancing application area of VO with unstructured environments [107]–[109].”
BackgroundFast Visual Odometry for a Low-Cost Underwater Embedded Stereo System †
Nawaf, M. M., Merad, D., Royer, J., Boï, J.-M., Saccone, M., Ellefi, M. B., & Drap, P. (2018) · Sensors · doi:10.3390/s18072313 · Cited by 17 · cited 1×
“Besides, VO is also widely used in underwater robots [119], [120], space exploration robots [121],”
BackgroundEvaggelos Spyrou, A., Iakovidis, D. K., Niafas, S., & Koulaouzidis, A. (2015) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2015.05.013 · Cited by 16 · cited 1×
“used VO techniques to track his location without additional sensors and hardware in the GI tract [125]–[127].”
MethodRobot Navigation in Unseen Spaces Using an Abstract Map
Talbot, B., Dayoub, F., Corke, P., & Wyeth, G. F. (2020) · IEEE Transactions on Cognitive and Developmental Systems · doi:10.1109/tcds.2020.2993855 · Cited by 16
Camera pose estimation for augmented reality in a small indoor dynamic scene
Frikha, R., Ejbali, R., & Zaied, M. (2017) · Journal of Electronic Imaging · doi:10.1117/1.jei.26.5.053029 · Cited by 15 · cited 1×
“systems usually use the camera as only sensors [131], [132].”
BackgroundDimas, G., Iakovidis, D. K., Karargyris, A., Ciuti, G., & Koulaouzidis, A. (2017) · Measurement Science and Technology · doi:10.1088/1361-6501/aa7ebf · Cited by 15
PoseConvGRU: A Monocular Approach for Visual Ego-motion Estimation by Learning
Zhai, G., Liu, L., Zhang, L., Liu, Y., & Jiang, Y. (2020) · Pattern Recognition · doi:10.1016/j.patcog.2019.107187 · Cited by 15
Simultaneous localization and mapping (SLAM) robotics techniques: a possible application in surgery
Scaradozzi, D., Zingaretti, S., & Ferrari, A. (2018) · Shanghai Chest · doi:10.21037/shc.2018.01.01 · Cited by 14 · cited 1×
“based medical applications, which can navigate outside or inside the patient’s body to refer position and discovered problems [124].”
BackgroundForward and Backward Visual Fusion Approach to Motion Estimation with High Robustness and Low Cost
Wang, K., Huang, X., Chen, J., Cao, C., Xiong, Z., & Chen, L. F. (2019) · Remote Sensing · doi:10.3390/rs11182139 · Cited by 13 · cited 1×
“autonomous and evolutionary robots, medical robots, augmented/mixed/virtual reality, and other complicated and emerging applications based on localization information, such as indoor and outdoor navigation, scene understanding, and space exploration [1]–[3].”
BackgroundSelf-citationInvariant Kalman filter application to optical flow based visual odometry for UAVs
Goppert, J., Yantek, S. M., & Hwang, I. (2017) · International Conference on Ubiquitous and Future Networks · doi:10.1109/icufn.2017.7993755 · Cited by 12 · cited 1×
“Compared with autonomous vehicles, using visual odometry into UAVs is more challenges due to the computational capability and 3D maneuverability [115], [116].”
MethodUnsupervised Ego-Motion and Dense Depth Estimation with Monocular Video
Xu, Y., Wang, Y., & Guo, L. (2018) · IEEE International Conference on Communication Technology (ICCT) · doi:10.1109/icct.2018.8600039 · Cited by 11
Teng, Q., Chen, Y., & Huang, C. (2018) · Future Internet · doi:10.3390/fi10100092 · Cited by 9 · cited 1×
“Besides, optical flow [52] and semantic information was also used into depth estimation problem [53].”
MethodEpipolar Geometry based Learning of Multi-view Depth and Ego-Motion from Monocular Sequences
Prasad, V., Das, D., & Bhowmick, B. (2018) · Indian Conference on Computer Vision, Graphics and Image Processing · doi:10.1145/3293353.3293427 · Cited by 9 · cited 2×
“To consider ambiguous pixels and obtain better geometric understanding ability, Prasad et al. [46] proposed an epipolar geometry-based approach, which gives each pixel a weight according to whether it is projected correctly.”
BackgroundMethodUnsupervised Learning of Accurate Camera Pose and Depth From Video Sequences With Kalman Filter
Wang, Y., & Xu, Y.-F. (2019) · IEEE Access · doi:10.1109/access.2019.2903871 · Cited by 9
Inferring sun direction to improve visual odometry: A deep learning approach
Peretroukhin, V., Clement, L., & Kelly, J. W. (2018) · The International Journal of Robotics Research · doi:10.1177/0278364917749732 · Cited by 8 · cited 1×
“[89] used a Bayesian convolutional neural network to generate sun”
MethodUnsupervised learning of monocular depth and ego-motion with space–temporal-centroid loss
Zhang, J., Su, Q., Liu, P., Xu, C., & Chen, Y. (2019) · International Journal of Machine Learning and Cybernetics · doi:10.1007/s13042-019-01020-6 · Cited by 7
Vision-based Global Localization of Unmanned Aerial Vehicles with Street View Images
Yan, X., Shi, Z., & Zhong, Y. (2018) · Chinese Control Conference (CCC) · doi:10.23919/chicc.2018.8483081 · Cited by 5 · cited 1×
“In order to obtain more robust performance, many researchers have tried to fusing data from multimodal sensors, such as IMU [115], [116].”
BackgroundMulti modal pose fusion for monocular flight with unmanned aerial vehicles
Khan, A., & Hebert, M. (2018) · IEEE Aerospace Conference (AERO) · doi:10.1109/aero.2018.8396806 · Cited by 2 · cited 1×
“In order to obtain more robust performance, many researches have tried to fusing data from multi-modal sensors, such as IMU [117], [118].”
BackgroundMonocular Camera Tracking Curve Optimization Algorithm in Augmented Reality
Gao, T., & Jiang, W. (2018) · Lecture notes on data engineering and communications technologies · doi:10.1007/978-3-030-02613-4_26 · Cited by 2 · cited 1×
“systems usually use the camera as only sensors [131], [132].”
Background
Research works printed but not linked: 13
- Campos, 2020, ORB-SLAM3: An accurate open-source library for visual, visual-inertial and multi-map SLAM
- 10.1007/978-3-030-01237-3_40 · DOI
- Ma, 2020, Progress in research on monocular visual odometry of autonomous vehicles, J. Jilin Univ., 50, 765
- 10.1145/3177853 · DOI
- Zhang, 2019, DFineNet: Ego-motion estimation and depth refinement from sparse, noisy depth input with RGB guidance
- Ramirez, 2018, Geometry meets semantics for semi-supervised monocular depth estimation
- Tosi, 2019, Learning monocular depth estimation infusing traditional stereo knowledge, 10.1109/CVPR.2019.01003 · DOI
- Mishchuk, 2017, Working hard to know your neighbor’s margins: Local descriptor learning loss, 30
- Cieslewski, 2018, Matching features without descriptors: Implicitly matched interest points
- Jiao, 2018, MagicVO: End-to-end monocular visual odometry through deep bi-directional recurrent convolutional neural network
- 10.1007/978-3-030-29911-8_35 · DOI
- Vijayanarasimhan, 2017, SfM-Net: Learning of structure and motion from video
- Wei, 2019, Instant motion tracking and its applications to augmented reality
Cited by
Sorted by citations
Vision-based navigation and guidance for agricultural autonomous vehicles and robots: A review
Bai, Y., Zhang, B., Xu, N., Zhou, J. C., Shi, J., & Diao, Z. (2022) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2022.107584 · Cited by 372
An Overview on Visual SLAM: From Tradition to Semantic
Chen, W.-F., Shang, G., Ji, A.-H., Zhou, C., Wang, X., Xu, C., Li, Z., & Hu, K. (2022) · Remote Sensing · doi:10.3390/rs14133010 · Cited by 217
Performance and Challenges of 3D Object Detection Methods in Complex Scenes for Autonomous Driving
Wang, K., Zhou, T., Li, X., & Ren, F. (2022) · IEEE Transactions on Intelligent Vehicles · doi:10.1109/tiv.2022.3213796 · Cited by 143
“R OBUST and accurate 3D object detection methods are essential for AD vehicles to understand the surrounding environment [1].”
MethodSelf-citationYu, S., Liu, X., Tan, Q., Wang, Z., & Zhang, B. (2024) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2024.109229 · Cited by 103
Deep Learning Techniques for Visual SLAM: A Survey
Mokssit, S., Licea, D. B., Guermah, B., & Ghogho, M. (2023) · IEEE Access · doi:10.1109/access.2023.3249661 · Cited by 81
“[32], [33], visual odometry estimation [34], or loop closure detection [22].”
Background
Metadata
- Authors
- Wang, K., Ma, S., Chen, J., Ren, F., & Lu, J.
- Affiliations
- Chongqing University (CN)
- Chongqing Normal University (CN)
- Changan Auto Company, Intelligent Vehicle R&D Institute, Chongqing, China
- Ford Motor Company (United States) (US)
- Venue
- IEEE Transactions on Cognitive and Developmental Systems
- Published
- 2022-03
- Volume
- 14
- Issue
- 1
- Pages
- 35-49
- ISSN
- 2379-8920 (ISSN-L)
- 2379-8939
- OpenAlex
- W3083208433
- Cited by
- 114
- References
- 127
- Citations by year
- 2026: 10 · 2025: 15 · 2024: 35 · 2023: 25 · 2022: 19
- FWCI
- 8.11
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- IEEE Transactions on Cognitive and Developmental Systems (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- National Natural Science Foundation of China (51605054)
- National Key Research and Development Program of China (SQ2020YFF0410766)
- Natural Science Foundation of Chongqing (cstc2020jcyj-msxmX0575)
- Chongqing Technology Innovation and Application Development Project (cstc2020jscxmsxmX0109)
- State Key Laboratory of Vehicle NVH and Safety Technology (NVHSKL-202008, NVHSKL-202010)
- Fundamental Research Funds for the Central Universities (2020CDJ-LHZZ-042)
- 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
- visual odometry, deep learning, relative localization, depth estimation, feature matching, pose estimation, mobile robots, medical robots, augmented reality, virtual reality
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