{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,25]],"date-time":"2026-06-25T12:40:42Z","timestamp":1782391242905,"version":"3.54.5"},"reference-count":34,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2023,1,25]],"date-time":"2023-01-25T00:00:00Z","timestamp":1674604800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Science and Technology Planning Project of Jinhua","award":["2022-1-096."],"award-info":[{"award-number":["2022-1-096."]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Recent developments in robotics have heightened the need for visual SLAM. Dynamic objects are a major problem in visual SLAM which reduces the accuracy of localization due to the wrong epipolar geometry. This study set out to find a new method to address the low accuracy of visual SLAM in outdoor dynamic environments. We propose an adaptive feature point selection system for outdoor dynamic environments. Initially, we utilize YOLOv5s with the attention mechanism to obtain a priori dynamic objects in the scene. Then, feature points are selected using an adaptive feature point selector based on the number of a priori dynamic objects and the percentage of a priori dynamic objects occupied in the frame. Finally, dynamic regions are determined using a geometric method based on Lucas-Kanade optical flow and the RANSAC algorithm. We evaluate the accuracy of our system using the KITTI dataset, comparing it to various dynamic feature point selection strategies and DynaSLAM. Experiments show that our proposed system demonstrates a reduction in both absolute trajectory error and relative trajectory error, with a maximum reduction of 39% and 30%, respectively, compared to other systems.<\/jats:p>","DOI":"10.3390\/s23031359","type":"journal-article","created":{"date-parts":[[2023,1,26]],"date-time":"2023-01-26T01:30:30Z","timestamp":1674696630000},"page":"1359","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":26,"title":["An Adaptive ORB-SLAM3 System for Outdoor Dynamic Environments"],"prefix":"10.3390","volume":"23","author":[{"given":"Qiuyu","family":"Zang","sequence":"first","affiliation":[{"name":"College of Mathematics and Computer Science, Zhejiang Normal University, Yingbin Avenue, Jinhua 321005, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9623-3949","authenticated-orcid":false,"given":"Kehua","family":"Zhang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Urban Rail Transit Intelligent Operation and Maintenance Technology & Equipment of Zhejiang Province, Zhejiang Normal University, Yingbin Avenue, Jinhua 321005, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ling","family":"Wang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Urban Rail Transit Intelligent Operation and Maintenance Technology & Equipment of Zhejiang Province, Zhejiang Normal University, Yingbin Avenue, Jinhua 321005, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lintong","family":"Wu","sequence":"additional","affiliation":[{"name":"College of Mathematics and Computer Science, Zhejiang Normal University, Yingbin Avenue, Jinhua 321005, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,1,25]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1004","DOI":"10.1109\/TRO.2018.2853729","article-title":"VINS-Mono: A Robust and Versatile Monocular Visual-Inertial State Estimator","volume":"34","author":"Qin","year":"2018","journal-title":"IEEE Trans. Robot."},{"key":"ref_2","unstructured":"Koestler, L., Yang, N., Zeller, N., and Cremers, D. (2022). Conference on Robot Learning, PMLR."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1147","DOI":"10.1109\/TRO.2015.2463671","article-title":"ORB-SLAM: A Versatile and Accurate Monocular SLAM System","volume":"31","author":"Montiel","year":"2015","journal-title":"IEEE Trans. Robot."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1255","DOI":"10.1109\/TRO.2017.2705103","article-title":"ORB-SLAM2: An Open-Source SLAM System for Monocular, Stereo, and RGB-D Cameras","volume":"33","year":"2017","journal-title":"IEEE Trans. Robot."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1874","DOI":"10.1109\/TRO.2021.3075644","article-title":"ORB-SLAM3: An Accurate Open-Source Library for Visual, Visual\u2013Inertial, and Multimap SLAM","volume":"37","author":"Campos","year":"2021","journal-title":"IEEE Trans. Robot."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"104992","DOI":"10.1016\/j.engappai.2022.104992","article-title":"A review of visual SLAM methods for autonomous driving vehicles","volume":"114","author":"Cheng","year":"2022","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"875","DOI":"10.1007\/s12559-018-9591-8","article-title":"Ongoing evolution of visual slam from geometry to deep learning: Challenges and opportunities","volume":"10","author":"Li","year":"2018","journal-title":"Cogn. Comput."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1007\/s10462-012-9365-8","article-title":"Visual simultaneous localization and mapping: A survey","volume":"43","year":"2015","journal-title":"Artif. Intell. Rev."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"882","DOI":"10.1108\/IR-01-2019-0001","article-title":"Fast and robust visual odometry with a low-cost IMU in dynamic environments","volume":"46","author":"Yao","year":"2019","journal-title":"Ind. Robot. Int. J. Robot. Res. Appl."},{"key":"ref_10","unstructured":"Zhang, J., Henein, M., Mahony, R., and Ila, V. (2020). VDO-SLAM: A visual dynamic object-aware SLAM system. arXiv."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Zhang, C., Huang, T., Zhang, R., and Yi, X. (2021). PLD-SLAM: A new RGB-D SLAM method with point and line features for indoor dynamic scene. ISPRS Int. J. -Geo-Inf., 10.","DOI":"10.3390\/ijgi10030163"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Yu, C., Liu, Z., Liu, X.J., Xie, F., Yang, Y., Wei, Q., and Fei, Q. (2018, January 1\u20135). DS-SLAM: A semantic visual SLAM towards dynamic environments. Proceedings of the 2018 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), Madrid, Spain.","DOI":"10.1109\/IROS.2018.8593691"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"4076","DOI":"10.1109\/LRA.2018.2860039","article-title":"DynaSLAM: Tracking, mapping, and inpainting in dynamic scenes","volume":"3","author":"Bescos","year":"2018","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"5191","DOI":"10.1109\/LRA.2021.3068640","article-title":"DynaSLAM II: Tightly-coupled multi-object tracking and SLAM","volume":"6","author":"Bescos","year":"2021","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"6011","DOI":"10.1007\/s00521-021-06764-3","article-title":"YOLO-SLAM: A semantic SLAM system towards dynamic environment with geometric constraint","volume":"34","author":"Wu","year":"2022","journal-title":"Neural Comput. Appl."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Liu, J., Gu, Q., Chen, D., and Yan, D. (2022). VSLAM method based on object detection in dynamic environments. Front. Neurorobotics, 16.","DOI":"10.3389\/fnbot.2022.990453"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Zhang, X., Zhang, R., and Wang, X. (2022). Visual slam mapping based on yolov5 in dynamic scenes. Appl. Sci., 12.","DOI":"10.3390\/app122211548"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Zhong, Y., Hu, S., Huang, G., Bai, L., and Li, Q. (2022). WF-SLAM: A Robust VSLAM for Dynamic Scenarios via Weighted Features. IEEE Sens. J.","DOI":"10.1109\/JSEN.2022.3169340"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Chen, B., Peng, G., He, D., Zhou, C., and Hu, B. (2021, January 22\u201324). Visual SLAM Based on Dynamic Object Detection. Proceedings of the 2021 33rd Chinese Control and Decision Conference (CCDC), Kunming, China.","DOI":"10.1109\/CCDC52312.2021.9602200"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"43563","DOI":"10.1109\/ACCESS.2020.2977684","article-title":"Dynamic scene semantics SLAM based on semantic segmentation","volume":"8","author":"Han","year":"2020","journal-title":"IEEE Access"},{"key":"ref_21","unstructured":"Wolf, L., Hassner, T., and Taigman, Y. (2008, January 10). Descriptor based methods in the wild. Proceedings of the Workshop on Faces in\u2019Real-Life\u2019images: Detection, Alignment, and Recognition, Marseille, France."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"20","DOI":"10.1186\/s42492-021-00086-w","article-title":"A survey: Which features are required for dynamic visual simultaneous localization and mapping?","volume":"4","author":"Xu","year":"2021","journal-title":"Vis. Comput. Ind. Biomed. Art"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"925","DOI":"10.1109\/TRO.2019.2909168","article-title":"Cubeslam: Monocular 3-d object slam","volume":"35","author":"Yang","year":"2019","journal-title":"IEEE Trans. Robot."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Qian, R., Lai, X., and Li, X. (2022). 3D object detection for autonomous driving: A survey. Pattern Recognit., 108796.","DOI":"10.1016\/j.patcog.2022.108796"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Chang, Z., Wu, H., Sun, Y., and Li, C. (2022). RGB-D Visual SLAM Based on Yolov4-Tiny in Indoor Dynamic Environment. Micromachines, 13.","DOI":"10.3390\/mi13020230"},{"key":"ref_26","first-page":"80","article-title":"Visual odometry: Part i: The first 30 years and fundamentals","volume":"18","author":"Fraundorfer","year":"2011","journal-title":"IEEE Robot. Autom. Mag."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1007\/s10846-022-01643-y","article-title":"Real-Time Artificial Intelligence Based Visual Simultaneous Localization and Mapping in Dynamic Environments\u2014A Review","volume":"105","author":"Okasha","year":"2022","journal-title":"J. Intell. Robot. Syst."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"110","DOI":"10.1016\/j.robot.2016.11.012","article-title":"Improving RGB-D SLAM in dynamic environments: A motion removal approach","volume":"89","author":"Sun","year":"2017","journal-title":"Robot. Auton. Syst."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"166528","DOI":"10.1109\/ACCESS.2019.2952161","article-title":"Sof-slam: A semantic visual slam for dynamic environments","volume":"7","author":"Cui","year":"2019","journal-title":"IEEE Access"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"162335","DOI":"10.1109\/ACCESS.2020.2991441","article-title":"DDL-SLAM: A robust RGB-D SLAM in dynamic environments combined with deep learning","volume":"8","author":"Ai","year":"2020","journal-title":"IEEE Access"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"331","DOI":"10.1007\/s41095-022-0271-y","article-title":"Attention mechanisms in computer vision: A survey","volume":"8","author":"Guo","year":"2022","journal-title":"Comput. Vis. Media"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Hou, Q., Zhou, D., and Feng, J. (2021, January 19). Coordinate attention for efficient mobile network design. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Online.","DOI":"10.1109\/CVPR46437.2021.01350"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1231","DOI":"10.1177\/0278364913491297","article-title":"Vision meets robotics: The kitti dataset","volume":"32","author":"Geiger","year":"2013","journal-title":"Int. J. Robot. Res."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Sturm, J., Engelhard, N., Endres, F., Burgard, W., and Cremers, D. (2012, January 7\u201312). A benchmark for the evaluation of RGB-D SLAM systems. Proceedings of the 2012 IEEE\/RSJ international conference on intelligent robots and systems, Vilamoura-Algarve, Portugal.","DOI":"10.1109\/IROS.2012.6385773"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/3\/1359\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T18:15:45Z","timestamp":1760120145000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/3\/1359"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,1,25]]},"references-count":34,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2023,2]]}},"alternative-id":["s23031359"],"URL":"https:\/\/doi.org\/10.3390\/s23031359","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,1,25]]}}}