{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,31]],"date-time":"2026-03-31T14:15:34Z","timestamp":1774966534650,"version":"3.50.1"},"reference-count":45,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2024,6,2]],"date-time":"2024-06-02T00:00:00Z","timestamp":1717286400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["52105148"],"award-info":[{"award-number":["52105148"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Visual Simultaneous Localization and Mapping (V-SLAM) plays a crucial role in the development of intelligent robotics and autonomous navigation systems. However, it still faces significant challenges in handling highly dynamic environments. The prevalent method currently used for dynamic object recognition in the environment is deep learning. However, models such as Yolov5 and Mask R-CNN require significant computational resources, which limits their potential in real-time applications due to hardware and time constraints. To overcome this limitation, this paper proposes ADM-SLAM, a visual SLAM system designed for dynamic environments that builds upon the ORB-SLAM2. This system integrates efficient adaptive feature point homogenization extraction, lightweight deep learning semantic segmentation based on an improved DeepLabv3, and multi-view geometric segmentation. It optimizes keyframe extraction, segments potential dynamic objects using contextual information with the semantic segmentation network, and detects the motion states of dynamic objects using multi-view geometric methods, thereby eliminating dynamic interference points. The results indicate that ADM-SLAM outperforms ORB-SLAM2 in dynamic environments, especially in high-dynamic scenes, where it achieves up to a 97% reduction in Absolute Trajectory Error (ATE). In various highly dynamic test sequences, ADM-SLAM outperforms DS-SLAM and DynaSLAM in terms of real-time performance and accuracy, proving its excellent adaptability.<\/jats:p>","DOI":"10.3390\/s24113578","type":"journal-article","created":{"date-parts":[[2024,6,3]],"date-time":"2024-06-03T05:58:00Z","timestamp":1717394280000},"page":"3578","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["ADM-SLAM: Accurate and Fast Dynamic Visual SLAM with Adaptive Feature Point Extraction, Deeplabv3pro, and Multi-View Geometry"],"prefix":"10.3390","volume":"24","author":[{"given":"Xiaotao","family":"Huang","sequence":"first","affiliation":[{"name":"School of Electromechanical Engineering, Guangdong University of Technology, Guangzhou 510006, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xingbin","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Electromechanical Engineering, Guangdong University of Technology, Guangzhou 510006, China"},{"name":"Guangdong Productivity Promotion Center, Guangzhou 510075, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ning","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Electromechanical Engineering, Guangdong University of Technology, Guangzhou 510006, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hongjie","family":"He","sequence":"additional","affiliation":[{"name":"School of Electromechanical Engineering, Guangdong University of Technology, Guangzhou 510006, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sang","family":"Feng","sequence":"additional","affiliation":[{"name":"School of Electromechanical Engineering, Guangdong University of Technology, Guangzhou 510006, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2024,6,2]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Ahmed, M.F., Masood, K., Fremont, V., and Fantoni, I. (2023). Active SLAM: A Review on Last Decade. Sensors, 23.","DOI":"10.3390\/s23198097"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"106153","DOI":"10.1016\/j.asoc.2020.106153","article-title":"A novel vSLAM framework with unsupervised semantic segmentation based on adversarial transfer learning","volume":"90","author":"Jin","year":"2020","journal-title":"Appl. Soft Comput."},{"key":"ref_3","unstructured":"Smith, R., Self, M., and Cheeseman, P. (1990). Autonomous Robot Vehicles, Springer."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Huo, J., Zhou, C., Yuan, B., Yang, Q., and Wang, L. (2023). Real-Time Dense Reconstruction with Binocular Endoscopy Based on StereoNet and ORB-SLAM. Sensors, 23.","DOI":"10.3390\/s23042074"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Klein, G., and Murray, D. (2007, January 13\u201316). Parallel tracking and mapping for small AR workspaces. Proceedings of the 2007 6th IEEE and ACM International Symposium on Mixed and Augmented Reality, Nara, Japan.","DOI":"10.1109\/ISMAR.2007.4538852"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"9086891","DOI":"10.1155\/2019\/9086891","article-title":"Pose Estimation of a Noncooperative Target Based on Monocular Visual SLAM","volume":"2019","author":"Lei","year":"2019","journal-title":"Int. J. Aerosp. Eng."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"773","DOI":"10.1007\/s10514-021-10002-z","article-title":"Semi-dense visual-inertial odometry and mapping for computationally constrained platforms","volume":"45","author":"Liu","year":"2021","journal-title":"Auton. Robot."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Yang, G., Chen, Z., Li, Y., and Su, Z. (2019). Rapid Relocation Method for Mobile Robot Based on Improved ORB-SLAM2 Algorithm. Remote Sens., 11.","DOI":"10.3390\/rs11020149"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Shan, Z., Li, R., and Schwertfeger, S. (2019). RGBD-Inertial Trajectory Estimation and Mapping for Ground Robots. Sensors, 19.","DOI":"10.3390\/s19102251"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1408","DOI":"10.1109\/LRA.2021.3140129","article-title":"Dm-vio: Delayed marginalization visual-inertial odometry","volume":"7","author":"Cremers","year":"2022","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1874","DOI":"10.1109\/TRO.2021.3075644","article-title":"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_12","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_13","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_14","doi-asserted-by":"crossref","first-page":"108403","DOI":"10.1016\/j.measurement.2020.108403","article-title":"A deep-learning real-time visual SLAM system based on multi-task feature extraction network and self-supervised feature points","volume":"168","author":"Li","year":"2021","journal-title":"Measurement"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"113084","DOI":"10.1016\/j.measurement.2023.113084","article-title":"Improving RGB-D SLAM accuracy in dynamic environments based on semantic and geometric constraints","volume":"217","author":"Wang","year":"2023","journal-title":"Measurement"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"23772","DOI":"10.1109\/ACCESS.2021.3050617","article-title":"RDS-SLAM: Real-time dynamic SLAM using semantic segmentation methods","volume":"9","author":"Liu","year":"2021","journal-title":"IEEE Access"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Gong, H., Gong, L., Ma, T., Sun, Z., and Li, L. (2023). AHY-SLAM: Toward Faster and More Accurate Visual SLAM in Dynamic Scenes Using Homogenized Feature Extraction and Object Detection Method. Sensors, 23.","DOI":"10.3390\/s23094241"},{"key":"ref_18","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"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"4635","DOI":"10.1109\/TIP.2023.3295741","article-title":"Secure Outsourced SIFT: Accurate and Efficient Privacy-Preserving Image SIFT Feature Extraction","volume":"32","author":"Liu","year":"2023","journal-title":"IEEE Trans. Image Process."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Zhang, J., Li, Y., Tai, A., Wen, X., and Jiang, J. (2022). Motion Video Recognition in Speeded-Up Robust Features Tracking. Electronics, 11.","DOI":"10.3390\/electronics11182959"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Chu, G., Peng, Y., and Luo, X. (2023). ALGD-ORB: An improved image feature extraction algorithm with adaptive threshold and local gray difference. PLoS ONE, 18.","DOI":"10.1371\/journal.pone.0293111"},{"key":"ref_22","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_23","first-page":"232","article-title":"Improved ORB feature extraction algorithm based on quadtree encoding","volume":"45","author":"Yu","year":"2018","journal-title":"Comput. Sci."},{"key":"ref_24","unstructured":"Brown, M., Szeliski, R., and Winder, S. (2005, January 20\u201326). Multi-image matching using multi-scale oriented patches. Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR\u201905), San Diego, CA, USA."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"145","DOI":"10.1016\/j.ins.2018.09.012","article-title":"Fast neighbor search by using revised kd tree","volume":"472","author":"Chen","year":"2019","journal-title":"Inf. Sci."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"335","DOI":"10.1007\/s11263-011-0431-5","article-title":"Evaluation of interest point detectors and feature descriptors for visual tracking","volume":"94","author":"Gauglitz","year":"2011","journal-title":"Int. J. Comput. Vis."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"8361","DOI":"10.1109\/TITS.2023.3264588","article-title":"A Comprehensive Implementation of Road Surface Classification for Vehicle Driving Assistance: Dataset, Models, and Deployment","volume":"24","author":"Zhao","year":"2023","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"111684","DOI":"10.1016\/j.asoc.2024.111684","article-title":"A machine vision approach with temporal fusion strategy for concrete vibration quality monitoring","volume":"160","author":"Li","year":"2024","journal-title":"Appl. Soft Comput."},{"key":"ref_29","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_30","doi-asserted-by":"crossref","first-page":"4244","DOI":"10.1109\/LRA.2022.3150854","article-title":"Simvodis++: Neural semantic visual odometry in dynamic environments","volume":"7","author":"Kim","year":"2022","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"550","DOI":"10.1109\/LRA.2020.3045647","article-title":"DymSLAM: 4D dynamic scene reconstruction based on geometrical motion segmentation","volume":"6","author":"Wang","year":"2020","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1016\/j.patrec.2018.02.020","article-title":"Efficient adaptive non-maximal suppression algorithms for homogeneous spatial keypoint distribution","volume":"106","author":"Bailo","year":"2018","journal-title":"Pattern Recognit. Lett."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"2925","DOI":"10.1109\/TCE.2023.3301067","article-title":"Low-Rank Tensor Regularized Graph Fuzzy Learning for Multi-View Data Processing","volume":"70","author":"Pan","year":"2024","journal-title":"IEEE Trans. Consum. Electron."},{"key":"ref_34","unstructured":"Chen, L.-C., Papandreou, G., Schroff, F., and Adam, H. (2017). Rethinking atrous convolution for semantic image segmentation. arXiv."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Chen, L.-C., Zhu, Y., Papandreou, G., Schroff, F., and Adam, H. (2018, January 8\u201314). Encoder-decoder with atrous separable convolution for semantic image segmentation. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01234-2_49"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"164546","DOI":"10.1109\/ACCESS.2020.3021739","article-title":"Semantic Segmentation of Litchi Branches Using DeepLabV3+ Model","volume":"8","author":"Peng","year":"2020","journal-title":"IEEE Access"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"92540","DOI":"10.1109\/ACCESS.2023.3294476","article-title":"DeepLabV3+ Vision Transformer for Visual Bird Sound Denoising","volume":"11","author":"Li","year":"2023","journal-title":"IEEE Access"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27\u201330). Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Chollet, F. (2017, January 21\u201326). Xception: Deep learning with depthwise separable convolutions. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.195"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., and Chen, L.C. (2018, January 18\u201323). Mobilenetv2: Inverted residuals and linear bottlenecks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00474"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"834","DOI":"10.1109\/TPAMI.2017.2699184","article-title":"Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs","volume":"40","author":"Chen","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"716","DOI":"10.1002\/tee.24004","article-title":"YVG-SLAM: Dynamic Feature Removal SLAM Algorithm Without A Priori Assumptions Based on Object Detection and View Geometry","volume":"19","author":"Li","year":"2024","journal-title":"IEEJ Trans. Electr. Electron. Eng."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"1157","DOI":"10.1177\/0278364915620033","article-title":"The Euroc Micro Aerial Vehicle Datasets","volume":"35","author":"Burri","year":"2016","journal-title":"Int. J. Robot. Res."},{"key":"ref_44","first-page":"9","article-title":"A Method for Evaluating the Uniformity of Image Feature Point Distribution","volume":"30","author":"Zhu","year":"2010","journal-title":"J. Daqing Norm. Univ."},{"key":"ref_45","unstructured":"Everingham, L.V.M., Williams, C.K.I., and Winn, J. (2024, May 29). The PASCAL Visual Object Classes Challenge 2012 (VOC2012). Available online: http:\/\/www.pascal-network.org\/challenges\/VOC\/voc2012\/workshop\/index.html."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/11\/3578\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T14:52:31Z","timestamp":1760107951000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/11\/3578"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,6,2]]},"references-count":45,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2024,6]]}},"alternative-id":["s24113578"],"URL":"https:\/\/doi.org\/10.3390\/s24113578","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,6,2]]}}}