Mobile QR Code QR CODE

2024

Acceptance Ratio

21%

References

1 
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2 
Klein G., Murray D., 2009, Parallel tracking and mapping on a camera phone, Proc. of IEEE International Symposium on Mixed and Augmented Reality (ISMAR), pp. 83-86DOI
3 
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4 
Engel J., Schöps T., Cremers D., 2014, LSD-SLAM: Large-scale direct monocular SLAM, Proc. of European Conference on Computer Vision (ECCV), pp. 834-849DOI
5 
Mur-Artal R., Montiel J. M. M., Tardós J. D., 2015, ORB-SLAM: A versatile and accurate monocular SLAM system, IEEE Transactions on Robotics, Vol. 31, No. 5, pp. 1147-1163DOI
6 
Mur-Artal R., 2017, ORB-SLAM2: An Open-Source SLAM System for Monocular, Stereo, and RGB-D Cameras, IEEE Transactions on Robotics, Vol. 33, No. 5DOI
7 
Harris C., Stephens M., 1988, A combined corner and edge detector, Proc. of Alvey Vision Conference, Vol. 15, No. 50, pp. 10-5244DOI
8 
Shi J., Tomasi , 1994, Good features to track, Proc. of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 593-600DOI
9 
Lowe D. G., 2004, Distinctive image features from scale-invariant keypoints, International Journal of Computer Vision, Vol. 60, pp. 91-110DOI
10 
Bay H., Tuytelars T., Gool L. V., 2006, SURF: Speeded up robust features, Proc. of European Conference on Computer Vision (ECCV)DOI
11 
Rosten E., Drummond T., 2006, Machine learning for high-speed corner detection, Proc. of European Conference on Computer Vision (ECCV)DOI
12 
Mair E., Häger G. D., Burschka D., Suppa M., Hirzinger G., 2010, Adaptive and generic corner detection based on the accelerated segment test, Proc. of European Conference on Computer Vision (ECCV)DOI
13 
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14 
Alcantarilla P. F., Bartoli A., Davison A. J., 2012, KAZE features, Proc. of European Conference on Computer Vision (ECCV)DOI
15 
Pumarola A., Vakhitov A., Agudo A., Sandeliu A., Moreno-Noguer F., 2017, PL-SLAM: Real-time monocular visual slam with points and lines, Proc. of 2017 IEEE International Conference on Robotics and Automation (ICRA), pp. 4503-4508DOI
16 
Gomez-Ojeda R., Moreno F.-A., Zuniga-Noël D., Scaramuzza D., Gonzalez-Jimenez J., 2019, PL-SLAM: A stereo slam system through the combination of points and line segments, IEEE Transactions on Robotics, Vol. 35, No. 3, pp. 734-746DOI
17 
Lee S. J., Hwnag S. S., 2019, Elaborate monocular point and line slam with robust initialization, Proc. of the IEEE/CVF International Conference on Computer Vision, pp. 1121-1129DOI
18 
Lim H., Kim Y., Jung K., Hu S., Myung H., 2021, Avoiding degeneracy for monocular visual slam with point and line features, Proc. of 2021 IEEE International Conference on Robotics and Automation (ICRA), pp. 11675-11681DOI
19 
Lim H., Jeon J., Myung H., 2022, UV-SLAM: Unconstrained line-based SLAM using vanishing points for structural mapping, IEEE Robotics and Automation Letters, Vol. 7, No. 2, pp. 1518-1525DOI
20 
Islam R., Habinuyllah H., Hossain T., 2023, AGRI-SLAM: A real-time stereo visual SLAM for agricultural environment, Autonomous Robots, Vol. 47, pp. 649-668DOI
21 
Yang H., Juan J., Gao Y., Sun X., Zhang X., 2023, UPLP-SLAM: unified point-line-plane feature fusion for RGB-D visual SLAM, Information Fusion, Vol. 96, pp. 51-65DOI
22 
Shu F., Wang J., Pagani A., Stricker D., 2023, Structure PLP-SLAM: Efficient sparse mapping and localization using point, line and plane for monocular, RGB-D and stereo cameras, Proc. of 2023 IEEE International Conference on Robotics and Automation (ICRA), pp. 2105-2112DOI
23 
Yan J., Zheng Y., Yang J., Mihaylove L., Yuan W., Gu F., 2024, PLPF‐VSLAM: An indoor visual SLAM with adaptive fusion of point‐line‐plane features, Journal of Field Robotics, Vol. 41, No. 1, pp. 50-67DOI
24 
Kang R., Shi J., Li X., Yiu L., Liu X., 2019, DF-SLAM: A deep-learning enhanced visual SLAM system based on deep local features, arXiv preprint arXiv:190107223DOI
25 
Li D., Shi X., Long Q., Liu S., Wang W., Wang F., 2020, DXSLAM: A robust and efficient visual SLAM system with deep features, Proc. of the 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)DOI
26 
Xu L., Feng C., Kamat V. R., Menassa C. C., 2020, A scene-adaptive descriptor for visual SLAM-based locating applications in built environments, Automation in Construction, Vol. 112DOI
27 
Bruno H. M. S., Colombini E. L., 2021, LIFT-SLAM: A deep-learning feature-based monocular visual SLAM method, Neurocomputing, Vol. 455, pp. 97-110DOI
28 
Li G., Yu L., Fei S., 2021, A deep-learning real-time visual slam system based on multi-task feature extraction network and self-supervised feature points, Measurement, Vol. 168, pp. 108403DOI
29 
Pfrommer B., Daniilidis K., 2019, TagSLAM: Robust SLAM with fiducial markers, arXiv preprint arXiv:1910.00679DOI
30 
Munoz-Salinas R., Marín-Jimenez M. J., Medina-Carnicer R., 2019, SPM-SLAM: Simultaneous localization and mapping with squared planar markers, Pattern Recognition, Vol. 86, pp. 156-171DOI
31 
Munoz-Salinas R., Medina-Carnicer R., 2020, UcoSLAM: Simultaneous localization and mapping by fusion of keypoints and squared planar markers, Pattern Recognition, Vol. 101, pp. 107193DOI
32 
Li B., Zou D., Sartori D., Pei L., Yu W., 2020, TextSLAM: Visual SLAM with planar text features, Proc. of 2020 IEEE International Conference on Robotics and Automation (ICRA)DOI
33 
Yu C., Liu Z., Liu X.-J., Xie F., Yang Y., Wei Q., 2018, DS-SLAM: A semantic visual SLAM towards dynamic environments, Proc. of the 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)DOI
34 
Xiao L., Wang J., Qiu X., Rong Z., Zou X., 2019, Dynamic-SLAM: Semantic monocular visual localization and mapping based on deep learning in dynamic environment, Robotics and Autonomous Systems, Vol. 117, pp. 1-16DOI
35 
Li A., Wang J., Xu M., Chen Z., 2021, DP-SLAM: A visual SLAM with moving probability towards dynamic environments, Information Sciences, Vol. 556, pp. 128-142DOI
36 
Kim U. H., Kim S.-H., Kim J.-H., 2022, SimVODIS++: Neural semantic visual odometry in dynamic environments, IEEE Robotics and Automation Letters, Vol. 7, No. 2, pp. 4244-4251DOI
37 
Yang S., Scherer S., 2019, CubeSLAM: Monocular 3-D object SLAM, IEEE Transactions on Robotics, Vol. 35, No. 4, pp. 925-938DOI
38 
Bescos B., Campos C., Tardós J. D., Neira J., 2021, DynaSLAM II: Tightly-coupled multi-object tracking and SLAM, IEEE Robotics and Automation Letters, Vol. 6, No. 3, pp. 5191-5198DOI
39 
Xu D., Vedaldi A., Henriques J. F., 2021, Moving SLAM: Fully unsupervised deep learning in non-rigid scenes, Proc. of the 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)DOI
40 
Sharma A., Dong W., Kaess M., 2021, Compositional and scalable object SLAM, Proc. of 2021 IEEE International Conference on Robotics and Automation (ICRA)DOI
41 
Sun Y., Hu J., Yun J., Liu Y., Bai D., Liu X., Zhao G., Liang G., Kong J., Chen B., 2022, Multi-objective location and mapping based on deep learning and visual SLAM, Sensors, Vol. 22, No. 19, pp. 7576DOI
42 
Liao Z., Hu Y., Zhang J., Qi X., Zhang X., Wang W., 2022, SO-SLAM: Semantic object SLAM with scale proportional and symmetrical texture constraints, IEEE Robotics and Automation Letters, Vol. 7, No. 2, pp. 4008-4015DOI
43 
He K., Gkioxari G., Dollár P., Girshick R., 2017, Mask R-CNN, Proc. of the IEEE International Conference on Computer VisionDOI
44 
Qin T., Yen Y., Zheng T., Chen Y., Chen Q., Su Q., 2021, A light-weight semantic map for visual localization towards autonomous driving, Proc. of 2021 IEEE International Conference on Robotics and Automation (ICRA)DOI
45 
Mildenhall B., 2021, NeRf: Representing scenes as neural radiance fields for view synthesis, Communications of the ACM, Vol. 65, No. 1, pp. 99-106DOI
46 
Sucar E., Liu S., Ortiz J., Davison A. J., 2021, iMAP: Implicit mapping and positioning in real-time, Proc. of 2021 IEEE/CVF International Conference on Computer Vision (ICCV)DOI
47 
Zhu Z., Peng S., Larsson V., Xu W., Bao H., Cui Z., 2022, Nice-SLAM: Neural implicit scalable encoding for SLAM, Proc. of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)DOI
48 
Ortiz J., Clegg A., Dong J., Sucar E., Novotny D., Zollhoefer M., Mukadam M., 2022, iSDF: Real-time neural signed distance fields for robot percaption, arXiv preprint arXiv:2204.02296DOI
49 
Ming Y., Ye W., Calway A., 2022, iDF-SLAM: End-to-end RGB-D SLAM with neural implicit mapping and deep feature tracking, arXiv preprint arXiv:2209.07919DOI
50 
Johari M. M., Carta C., Fleuret F., 2023, ESLAM: Efficient dense SLAM system based on hybrid representation of signed distance fields, Proc. of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)DOI
51 
Kruzhkov E., Savinykh A., Karpyshev P., Murenkov M., Yudin E., Potapov A., 2022, MeSLAM: Memory efficient slam based on neural fields, Proc. of 2022 IEEE International Conference on Systems, Man, and Cybernetics (SMC)DOI
52 
Yang X., Li H., Zhai H., Ming Y., Liu Y., Zhang G., 2022, Vox-fusion: Dense tracking and mapping with voxel-based neural implicit representation, Proc. of IEEE International Symposium on Mixed and Augmented Reality (ISMAR)DOI
53 
Kong X., Liu S., Taher M., Davison A. J., 2023, vMAP: Vectorised object mapping for neural field SLAM, arXiv preprint arXiv:2302.01838DOI
54 
Wang H., Wang J., Agapito L., 2023, Co-SLAM: Joint coordinate and sparse parametric encodings for neural real-time SLAM, Proc. of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)DOI
55 
Rosinol A., Leonard J. J., Carlone L., 2023, NeRF-SLAM: Real-time dense monocular SLAM with neural radiance fields, Proc. of the 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)DOI
56 
Chung C.-M., Tseng C.-M., Hsu Y.-C., Shi X.-Q., Hua Y.-H., Yeh J.-F., 2023, Orbeez-SLAM: A realtime monocular visual slam with orb features and nerfrealized mapping, Proc. of 2023 IEEE International Conference on Robotics and Automation (ICRA)DOI
57 
Zhu Z., Peng S., Larsson V., Cui Z., Oswald M. R., Geiger A., 2024, Nicer-slam: Neural implicit scene encoding for RGB SLAM, Proc. of 2024 International Conference on 3D Vision (3DV)DOI
58 
Li Y., Yunus R., Brasch N., Navab N., Tombari F., 2021, RGB-D SLAM with structural regularities, Proc. of 2021 IEEE International Conference on Robotics and Automation (ICRA)DOI