{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,30]],"date-time":"2025-10-30T07:11:44Z","timestamp":1761808304769,"version":"build-2065373602"},"reference-count":45,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2019,8,26]],"date-time":"2019-08-26T00:00:00Z","timestamp":1566777600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The important problem of Simultaneous Localization and Mapping (SLAM) in dynamic environments is less studied than the counterpart problem in static settings. In this paper, we present a solution for the feature-based SLAM problem in dynamic environments. We propose an algorithm that integrates SLAM with multi-target tracking (SLAMMTT) using a robust feature-tracking algorithm for dynamic environments. A novel implementation of RANdomSAmple Consensus (RANSAC) method referred to as multilevel-RANSAC (ML-RANSAC) within the Extended Kalman Filter (EKF) framework is applied for multi-target tracking (MTT). We also apply machine learning to detect features from the input data and to distinguish moving from stationary objects. The data stream from LIDAR and vision sensors are fused in real-time to detect objects and depth information. A practical experiment is designed to verify the performance of the algorithm in a dynamic environment. The unique feature of this algorithm is its ability to maintain tracking of features even when the observations are intermittent whereby many reported algorithms fail in such situations. Experimental validation indicates that the algorithm is able to perform consistent estimates in a fast and robust manner suggesting its feasibility for real-time applications.<\/jats:p>","DOI":"10.3390\/s19173699","type":"journal-article","created":{"date-parts":[[2019,8,26]],"date-time":"2019-08-26T04:38:23Z","timestamp":1566794303000},"page":"3699","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":41,"title":["SLAM in Dynamic Environments: A Deep Learning Approach for Moving Object Tracking Using ML-RANSAC Algorithm"],"prefix":"10.3390","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3692-2168","authenticated-orcid":false,"given":"Masoud S.","family":"Bahraini","sequence":"first","affiliation":[{"name":"Department of Mechanical Engineering, Sirjan University of Technology, Sirjan 78137-33385, Iran"}]},{"given":"Ahmad B.","family":"Rad","sequence":"additional","affiliation":[{"name":"School of Mechatronic Systems Engineering, Simon Fraser University, Surrey, BC V3T 0A3, Canada"}]},{"given":"Mohammad","family":"Bozorg","sequence":"additional","affiliation":[{"name":"Faculty of Mechanical Engineering, Yazd University, Yazd 89195-741, Iran"}]}],"member":"1968","published-online":{"date-parts":[[2019,8,26]]},"reference":[{"key":"ref_1","first-page":"35","article-title":"A New Adaptive UKF Algorithm to Improve the Accuracy of SLAM","volume":"5","author":"Bahraini","year":"2019","journal-title":"Int. J. Robot. Theory Appl."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Bahraini, M.S. (2019). On the Efficiency of SLAM Using Adaptive Unscented Kalman Filter. Iran. J. Sci. Technol. Trans. Mech. Eng., 1\u20139.","DOI":"10.1007\/s40997-019-00294-z"},{"key":"ref_3","unstructured":"Ho, T.S., Fai, Y.C., and Ming, E.S.L. (June, January 31). Simultaneous Localization and Mapping Survey Based on Filtering Techniques. Proceedings of the 10th Asian Control Conference (ASCC), Kota Kinabalu, Malaysia."},{"key":"ref_4","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_5","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1002\/rob.21620","article-title":"Multiple-Robot Simultaneous Localization and Mapping: A Review","volume":"33","author":"Saeedi","year":"2016","journal-title":"J. Field Robot."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1309","DOI":"10.1109\/TRO.2016.2624754","article-title":"Past, Present, and Future of Simultaneous Localization and Mapping: Toward the Robust-Perception Age","volume":"32","author":"Cadena","year":"2016","journal-title":"IEEE Trans. Robot."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Zaffar, M., Ehsan, S., Stolkin, R., and Maier, K.M. (2018). Sensors, SLAM and Long-term Autonomy: A Review. arXiv.","DOI":"10.1109\/AHS.2018.8541483"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"889","DOI":"10.1177\/0278364907081229","article-title":"Simultaneous Localization, Mapping and Moving Object Tracking","volume":"26","author":"Wang","year":"2007","journal-title":"Int. J. Robot. Res."},{"key":"ref_9","unstructured":"Lin, K.-H., and Wang, C.-C. (2010, January 18\u201322). Stereo-based simultaneous localization, mapping and moving object tracking. Proceedings of the 2010 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), Taipei, Taiwan."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"58","DOI":"10.1016\/j.inffus.2010.01.004","article-title":"Grid-based localization and local mapping with moving object detection and tracking","volume":"12","author":"Vu","year":"2011","journal-title":"Inf. Fusion"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TASE.2015.2426203","article-title":"Exploiting Moving Objects: Multi-Robot Simultaneous Localization and Tracking","volume":"13","author":"Chang","year":"2016","journal-title":"IEEE Trans. Autom. Sci. Eng."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Baig, Q., Aycard, O., Vu, T.D., and Fraichard, T. (2011, January 5\u20139). Fusion between laser and stereo vision data for moving objects tracking in intersection like scenario. Proceedings of the Intelligent Vehicles Symposium (IV), Baden-Baden, Germany.","DOI":"10.1109\/IVS.2011.5940576"},{"key":"ref_13","unstructured":"Krizhevsky, A., Sutskever, I., and Hinton, G.E. (2012, January 3\u20138). Imagenet classification with deep convolutional neural networks. Proceedings of the Advances in Neural Information Processing Systems, Lake Tahoe, NV, USA."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"696","DOI":"10.1002\/rob.20312","article-title":"LIDAR and vision-based pedestrian detection system","volume":"26","author":"Premebida","year":"2009","journal-title":"J. Field Robot."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Angelova, A., Krizhevsky, A., and Vanhoucke, V. (2015, January 26\u201330). Pedestrian detection with a large-field-of-view deep network. Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), Washington, DC, USA.","DOI":"10.1109\/ICRA.2015.7139256"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Angelova, A., Krizhevsky, A., Vanhoucke, V., Ogale, A.S., and Ferguson, D. (2015, January 7\u201310). Real-Time Pedestrian Detection with Deep Network Cascades. Proceedings of the BMVC, Swansea, UK.","DOI":"10.5244\/C.29.32"},{"key":"ref_17","unstructured":"Chen, X., Kundu, K., Zhang, Z., Ma, H., Fidler, S., and Urtasun, R. (July, January 26). Monocular 3d object detection for autonomous driving. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA."},{"key":"ref_18","unstructured":"Eitel, A., Springenberg, J.T., Spinello, L., Riedmiller, M., and Burgard, W. (October, January 28). Multimodal deep learning for robust rgb-d object recognition. Proceedings of the IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), Hamburg, Germany."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Gupta, S., Girshick, R., Arbel\u00e1ez, P., and Malik, J. (2014, January 6\u201312). Learning rich features from RGB-D images for object detection and segmentation. Proceedings of the European Conference on Computer Vision, Zurich, Switzerland.","DOI":"10.1007\/978-3-319-10584-0_23"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Islam, M.M., Hu, G., and Liu, Q. (2018). Online Model Updating and Dynamic Learning Rate-Based Robust Object Tracking. Sensors, 18.","DOI":"10.3390\/s18072046"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"5423","DOI":"10.1109\/JIOT.2019.2902141","article-title":"Deep Learning-Based Multiple Object Visual Tracking on Embedded System for IoT and Mobile Edge Computing Applications","volume":"6","author":"Brea","year":"2019","journal-title":"IEEE Int. Things J."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Scheidegger, S., Benjaminsson, J., Rosenberg, E., Krishnan, A., and Granstr\u00f6m, K. (2018, January 26\u201330). Mono-camera 3d multi-object tracking using deep learning detections and pmbm filtering. Proceedings of the IEEE Intelligent Vehicles Symposium (IV), Suzhou, China.","DOI":"10.1109\/IVS.2018.8500454"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1016\/j.neucom.2018.01.092","article-title":"Computer vision and deep learning techniques for pedestrian detection and tracking: A survey","volume":"300","author":"Brunetti","year":"2018","journal-title":"Neurocomputing"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1134\/S1054661816010065","article-title":"A survey of deep learning methods and software tools for image classification and object detection","volume":"26","author":"Druzhkov","year":"2016","journal-title":"Pattern Recognit. Image Anal."},{"key":"ref_25","unstructured":"Tai, L., and Liu, M. (2016). Deep-learning in Mobile Robotics-from Perception to Control Systems: A Survey on Why and Why not. arXiv."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Amditis, A., Thomaidis, G., Maroudis, P., Lytrivis, P., and Karaseitanidis, G. (2012). Multiple Hypothesis Tracking Implementation. Laser Scanner Technol., 199.","DOI":"10.5772\/33583"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Niedfeldt, P.C., and Beard, R.W. (2014, January 4\u20136). Multiple target tracking using recursive RANSAC. Proceedings of the IEEE American Control Conference, Portland, OR, USA.","DOI":"10.1109\/ACC.2014.6859273"},{"key":"ref_28","unstructured":"Bar-Shalom, Y., Li, X.R., and Kirubarajan, T. (2004). Estimation with Applications to Tracking and Navigation: Theory Algorithms and Software, John Wiley & Sons."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Raguram, R., Frahm, J.-M., and Pollefeys, M. (2008, January 12\u201318). A comparative analysis of RANSAC techniques leading to adaptive real-time random sample consensus. Proceedings of the European Conference on Computer Vision, Marseille, France.","DOI":"10.1007\/978-3-540-88688-4_37"},{"key":"ref_30","unstructured":"Niedfeldt, P.C. (2014). Recursive-RANSAC: A Novel Algorithm for Tracking Multiple Targets in Clutter. [Ph.D. Thesis, Brigham Young University]."},{"key":"ref_31","first-page":"1","article-title":"Data Association for Multi-Object Visual Tracking","volume":"6","author":"Betke","year":"2016","journal-title":"Synth. Lect. Comput. Vis."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1007\/s10514-005-0606-4","article-title":"Mobile Robot Simultaneous Localization and Mapping in Dynamic Environments","volume":"19","author":"Wolf","year":"2005","journal-title":"Auton. Robot."},{"key":"ref_33","unstructured":"Migliore, D., Rigamonti, R., Marzorati, D., Matteucci, M., and Sorrenti, D.G. (2009, January 12). Use a single camera for simultaneous localization and mapping with mobile object tracking in dynamic environments. Proceedings of the International Workshop on Safe Navigation in Open and Dynamic Environments Application to Autonomous Vehicles, Kobe, Japan."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Darms, M., Rybski, P., and Urmson, C. (2008, January 4\u20136). Classification and tracking of dynamic objects with multiple sensors for autonomous driving in urban environments. Proceedings of the IEEE Intelligent Vehicles Symposium, Eindhoven, The Netherlands.","DOI":"10.1109\/IVS.2008.4621259"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Azim, A., and Aycard, O. (2014, January 8\u201311). Layer-based supervised classification of moving objects in outdoor dynamic environment using 3D laser scanner. Proceedings of the IEEE Intelligent Vehicles Symposium Proceedings, Ypsilanti, MI, USA.","DOI":"10.1109\/IVS.2014.6856558"},{"key":"ref_36","first-page":"1","article-title":"Multiple Sensor Fusion and Classification for Moving Object Detection and Tracking","volume":"17","author":"Aycard","year":"2016","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_37","first-page":"37","article-title":"Visual SLAM and structure from motion in dynamic environments: A survey","volume":"51","author":"Saputra","year":"2018","journal-title":"ACM Comput. Surv. (CSUR)"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"105","DOI":"10.1016\/j.mechatronics.2017.12.002","article-title":"SLAM in dynamic environments via ML-RANSAC","volume":"49","author":"Bahraini","year":"2018","journal-title":"Mechatronics"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1002\/rob.21430","article-title":"Moving object detection with laser scanners","volume":"30","author":"Mertz","year":"2013","journal-title":"J. Field Robot."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Girshick, R., Donahue, J., Darrell, T., and Malik, J. (2014, January 24\u201327). Rich feature hierarchies for accurate object detection and semantic segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Columbus, OH, USA.","DOI":"10.1109\/CVPR.2014.81"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"242","DOI":"10.1109\/70.938382","article-title":"Optimization of the simultaneous localization and map-building algorithm for real-time implementation","volume":"17","author":"Guivant","year":"2001","journal-title":"IEEE Trans. Robot. Autom."},{"key":"ref_42","unstructured":"Bailey, T. (2002). Mobile Robot Localisation AND Mapping in Extensive Outdoor Environments, The University of Sydney."},{"key":"ref_43","unstructured":"De Silva, V., Roche, J., and Kondoz, A. (2018). Fusion of LiDAR and camera sensor data for environment sensing in driverless vehicles. arXiv."},{"key":"ref_44","unstructured":"Krizhevsky, A., and Hinton, G. (2009). Learning Multiple Layers of Features from Tiny Images, University of Toronto."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"592","DOI":"10.1049\/iet-spr.2015.0389","article-title":"Extended target tracking filter with intermittent observations","volume":"10","author":"Shi","year":"2016","journal-title":"IET Signal Process."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/19\/17\/3699\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T13:13:59Z","timestamp":1760188439000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/19\/17\/3699"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,8,26]]},"references-count":45,"journal-issue":{"issue":"17","published-online":{"date-parts":[[2019,9]]}},"alternative-id":["s19173699"],"URL":"https:\/\/doi.org\/10.3390\/s19173699","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2019,8,26]]}}}