{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,26]],"date-time":"2026-02-26T20:59:45Z","timestamp":1772139585431,"version":"3.50.1"},"reference-count":64,"publisher":"MDPI AG","issue":"16","license":[{"start":{"date-parts":[[2024,8,11]],"date-time":"2024-08-11T00:00:00Z","timestamp":1723334400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003052","name":"Technology Innovation Program","doi-asserted-by":"publisher","award":["20018198"],"award-info":[{"award-number":["20018198"]}],"id":[{"id":"10.13039\/501100003052","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Ministry of Trade, Industry &amp; Energy (MOTIE, Korea)","award":["20018198"],"award-info":[{"award-number":["20018198"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>To achieve Level 4 and above autonomous driving, a robust and stable autonomous driving system is essential to adapt to various environmental changes. This paper aims to perform vehicle pose estimation, a crucial element in forming autonomous driving systems, more universally and robustly. The prevalent method for vehicle pose estimation in autonomous driving systems relies on Real-Time Kinematic (RTK) sensor data, ensuring accurate location acquisition. However, due to the characteristics of RTK sensors, precise positioning is challenging or impossible in indoor spaces or areas with signal interference, leading to inaccurate pose estimation and hindering autonomous driving in such scenarios. This paper proposes a method to overcome these challenges by leveraging objects registered in a high-precision map. The proposed approach involves creating a semantic high-definition (HD) map with added objects, forming object-centric features, recognizing locations using these features, and accurately estimating the vehicle\u2019s pose from the recognized location. This proposed method enhances the precision of vehicle pose estimation in environments where acquiring RTK sensor data is challenging, enabling more robust and stable autonomous driving. The paper demonstrates the proposed method\u2019s effectiveness through simulation and real-world experiments, showcasing its capability for more precise pose estimation.<\/jats:p>","DOI":"10.3390\/s24165191","type":"journal-article","created":{"date-parts":[[2024,8,12]],"date-time":"2024-08-12T11:23:46Z","timestamp":1723461826000},"page":"5191","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["An Object-Centric Hierarchical Pose Estimation Method Using Semantic High-Definition Maps for General Autonomous Driving"],"prefix":"10.3390","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1676-7575","authenticated-orcid":false,"given":"Jeong-Won","family":"Pyo","sequence":"first","affiliation":[{"name":"Department of Electrical and Computer Engineering, College of Information and Communication Engineering, Sungkyunkwan University, Suwon 16419, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2715-4865","authenticated-orcid":false,"given":"Jun-Hyeon","family":"Choi","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, College of Information and Communication Engineering, Sungkyunkwan University, Suwon 16419, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5816-0088","authenticated-orcid":false,"given":"Tae-Yong","family":"Kuc","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, College of Information and Communication Engineering, Sungkyunkwan University, Suwon 16419, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2024,8,11]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Neven, D., De Brabandere, B., Georgoulis, S., Proesmans, M., and Van Gool, L. (2018, January 26\u201330). Towards end-to-end lane detection: An instance segmentation approach. Proceedings of the 2018 IEEE Intelligent Vehicles Symposium (IV), Changshu, China.","DOI":"10.1109\/IVS.2018.8500547"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Yu, Z., Ren, X., Huang, Y., Tian, W., and Zhao, J. (2020, January 20\u201323). Detecting lane and road markings at a distance with perspective transformer layers. Proceedings of the 2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC), Rhodes, Greece.","DOI":"10.1109\/ITSC45102.2020.9294383"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Zheng, T., Fang, H., Zhang, Y., Tang, W., Yang, Z., Liu, H., and Cai, D. (2021, January 2\u20139). Resa: Recurrent feature-shift aggregator for lane detection. Proceedings of the AAAI Conference on Artificial Intelligence, Virtual.","DOI":"10.1609\/aaai.v35i4.16469"},{"key":"ref_4","unstructured":"Hou, Y., Ma, Z., Liu, C., and Loy, C.C. (November, January 27). Learning lightweight lane detection cnns by self attention distillation. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Seoul, Repuiblic of Korea."},{"key":"ref_5","unstructured":"Paszke, A., Chaurasia, A., Kim, S., and Culurciello, E. (2016). Enet: A deep neural network architecture for real-time semantic segmentation. arXiv."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Philion, J. (2019, January 16\u201320). Fastdraw: Addressing the long tail of lane detection by adapting a sequential prediction network. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.01185"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Liu, T., Chen, Z., Yang, Y., Wu, Z., and Li, H. (November, January 19). Lane detection in low-light conditions using an efficient data enhancement: Light conditions style transfer. Proceedings of the 2020 IEEE Intelligent Vehicles Symposium (IV), Las Vegas, NV, USA.","DOI":"10.1109\/IV47402.2020.9304613"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1543","DOI":"10.1109\/TITS.2020.3030767","article-title":"Fusionlane: Multi-sensor fusion for lane marking semantic segmentation using deep neural networks","volume":"23","author":"Yin","year":"2020","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Khanum, A., Lee, C.Y., and Yang, C.S. (2022). Deep-learning-based network for lane following in autonomous vehicles. Electronics, 11.","DOI":"10.3390\/electronics11193084"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Waykole, S., Shiwakoti, N., and Stasinopoulos, P. (2022). Performance Evaluation of Lane Detection and Tracking Algorithm Based on Learning-Based Approach for Autonomous Vehicle. Sustainability, 14.","DOI":"10.3390\/su141912100"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Liu, X., Ji, W., You, J., Fakhri, G.E., and Woo, J. (2020, January 14\u201319). Severity-aware semantic segmentation with reinforced wasserstein training. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.01258"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Chen, Y., Li, W., and Van Gool, L. (2018, January 18\u201322). Road: Reality oriented adaptation for semantic segmentation of urban scenes. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00823"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Li, Y., Shi, J., and Li, Y. (2022). Real-Time Semantic Understanding and Segmentation of Urban Scenes for Vehicle Visual Sensors by Optimized DCNN Algorithm. Appl. Sci., 12.","DOI":"10.3390\/app12157811"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Aksoy, E.E., Baci, S., and Cavdar, S. (November, January 19). Salsanet: Fast road and vehicle segmentation in lidar point clouds for autonomous driving. Proceedings of the 2020 IEEE Intelligent Vehicles Symposium (IV), Las Vegas, NV, USA.","DOI":"10.1109\/IV47402.2020.9304694"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Cortinhal, T., Tzelepis, G., and Erdal Aksoy, E. (2020, January 5\u20137). Salsanext: Fast, uncertainty-aware semantic segmentation of lidar point clouds. Proceedings of the Advances in Visual Computing: 15th International Symposium, ISVC 2020, San Diego, CA, USA. Proceedings, Part II 15.","DOI":"10.1007\/978-3-030-64559-5_16"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Florea, H., Petrovai, A., Giosan, I., Oniga, F., Varga, R., and Nedevschi, S. (2022). Enhanced perception for autonomous driving using semantic and geometric data fusion. Sensors, 22.","DOI":"10.3390\/s22135061"},{"key":"ref_17","unstructured":"On-Road Automated Vehicle Standards Committee (2014). Taxonomy and Definitions for Terms Related to On-Road Motor Vehicle Automated Driving Systems, SAE International."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Chen, X., Hu, W., Zhang, L., Shi, Z., and Li, M. (2018). Integration of low-cost GNSS and monocular cameras for simultaneous localization and mapping. Sensors, 18.","DOI":"10.3390\/s18072193"},{"key":"ref_19","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_20","doi-asserted-by":"crossref","unstructured":"Cai, H., Hu, Z., Huang, G., Zhu, D., and Su, X. (2018). Integration of GPS, monocular vision, and high definition (HD) map for accurate vehicle localization. Sensors, 18.","DOI":"10.3390\/s18103270"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"20779","DOI":"10.3390\/s150820779","article-title":"GPS\/DR error estimation for autonomous vehicle localization","volume":"15","author":"Lee","year":"2015","journal-title":"Sensors"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"3162","DOI":"10.3390\/s120303162","article-title":"Monocular camera\/IMU\/GNSS integration for ground vehicle navigation in challenging GNSS environments","volume":"12","author":"Chu","year":"2012","journal-title":"Sensors"},{"key":"ref_23","unstructured":"Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., and Koltun, V. (2017, January 13\u201315). CARLA: An open urban driving simulator. Proceedings of the Conference on Robot Learning, PMLR, Mountain View, CA, USA."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Hemmati, M., Biglari-Abhari, M., and Niar, S. (2022). Adaptive real-time object detection for autonomous driving systems. J. Imaging, 8.","DOI":"10.3390\/jimaging8040106"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Caesar, H., Bankiti, V., Lang, A.H., Vora, S., Liong, V.E., Xu, Q., Krishnan, A., Pan, Y., Baldan, G., and Beijbom, O. (2020, January 13\u201319). nuscenes: A multimodal dataset for autonomous driving. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.01164"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Pan, X., Shi, J., Luo, P., Wang, X., and Tang, X. (2018, January 2\u20137). Spatial as deep: Spatial cnn for traffic scene understanding. Proceedings of the AAAI Conference on Artificial Intelligence, New Orleans, LA, USA.","DOI":"10.1609\/aaai.v32i1.12301"},{"key":"ref_27","unstructured":"Yu, F., Xian, W., Chen, Y., Liu, F., Liao, M., Madhavan, V., and Darrell, T. (2018). Bdd100k: A diverse driving video database with scalable annotation tooling. arXiv."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"3200","DOI":"10.1109\/LRA.2019.2927096","article-title":"A neurologically inspired sequence processing model for mobile robot place recognition","volume":"4","author":"Neubert","year":"2019","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Uy, M.A., and Lee, G.H. (2018, January 18\u201322). Pointnetvlad: Deep point cloud based retrieval for large-scale place recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00470"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Peng, G., Yue, Y., Zhang, J., Wu, Z., Tang, X., and Wang, D. (June, January 30). Semantic reinforced attention learning for visual place recognition. Proceedings of the 2021 IEEE International Conference on Robotics and Automation (ICRA), Xi\u2019an, China.","DOI":"10.1109\/ICRA48506.2021.9561812"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Garg, S., Suenderhauf, N., and Milford, M. (2018). Lost? appearance-invariant place recognition for opposite viewpoints using visual semantics. arXiv.","DOI":"10.15607\/RSS.2018.XIV.022"},{"key":"ref_32","unstructured":"Chanca\u02d9n, M., and Milford, M. (2020). DeepSeqSLAM: A trainable CNN+ RNN for joint global description and sequence-based place recognition. arXiv."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"4520","DOI":"10.1109\/LRA.2022.3150505","article-title":"EchoVPR: Echo state networks for visual place recognition","volume":"7","author":"Scerri","year":"2022","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_34","first-page":"560","article-title":"Linknet: Relational embedding for scene graph","volume":"31","author":"Woo","year":"2018","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Yang, J., Lu, J., Lee, S., Batra, D., and Parikh, D. (2018, January 8\u201314). Graph r-cnn for scene graph generation. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01246-5_41"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Zellers, R., Yatskar, M., Thomson, S., and Choi, Y. (2018, January 18\u201323). Neural motifs: Scene graph parsing with global context. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00611"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Zhang, J., Shih, K.J., Elgammal, A., Tao, A., and Catanzaro, B. (2019, January 18\u201323). Graphical contrastive losses for scene graph parsing. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2019.01180"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Li, Y., Ouyang, W., Zhou, B., Wang, K., and Wang, X. (2017, January 22\u201329). Scene graph generation from objects, phrases and region captions. Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.142"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Arandjelovic, R., Gronat, P., Torii, A., Pajdla, T., and Sivic, J. (2016, January 27\u201330). NetVLAD: CNN architecture for weakly supervised place recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.572"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Hausler, S., Garg, S., Xu, M., Milford, M., and Fischer, T. (2021, January 20\u201325). Patch-netvlad: Multi-scale fusion of locally-global descriptors for place recognition. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Nashville, TN, USA.","DOI":"10.1109\/CVPR46437.2021.01392"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Gu, J., Zhao, H., Lin, Z., Li, S., Cai, J., and Ling, M. (2019, January 15\u201320). Scene graph generation with external knowledge and image reconstruction. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00207"},{"key":"ref_42","unstructured":"Cong, Y., Liao, W., Ackermann, H., Rosenhahn, B., and Yang, M.Y. (2019, January 15\u201320). Spatial-temporal transformer for dynamic scene graph generation. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Long Beach, CA, USA."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Cui, Z., Xu, C., Zheng, W., and Yang, J. (2018, January 22\u201326). Context-dependent diffusion network for visual relationship detection. Proceedings of the 26th ACM International Conference on Multimedia, Seoul, Republic of Korea.","DOI":"10.1145\/3240508.3240668"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Zareian, A., Karaman, S., and Chang, S.F. (2020, January 23\u201328). Bridging knowledge graphs to generate scene graphs. Proceedings of the Computer Vision\u2013ECCV 2020: 16th European Conference, Glasgow, UK. Proceedings; Part XXIII 16.","DOI":"10.1007\/978-3-030-58592-1_36"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Suhail, M., Mittal, A., Siddiquie, B., Broaddus, C., Eledath, J., Medioni, G., and Sigal, L. (2021, January 19\u201325). Energy-based learning for scene graph generation. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Virtual.","DOI":"10.1109\/CVPR46437.2021.01372"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Vidanapathirana, K., Ramezani, M., Moghadam, P., Sridharan, S., and Fookes, C. (2022, January 23\u201327). LoGG3D-Net: Locally guided global descriptor learning for 3D place recognition. Proceedings of the 2022 International Conference on Robotics and Automation (ICRA), Philadelphia, PA, USA.","DOI":"10.1109\/ICRA46639.2022.9811753"},{"key":"ref_47","unstructured":"Fan, Z., Song, Z., Zhang, W., Liu, H., He, J., and Du, X. (2021). Attentive rotation invariant convolution for point cloud-based large scale place recognition. arXiv."},{"key":"ref_48","unstructured":"Qi, C.R., Su, H., Mo, K., and Guibas, L.J. (2017, January 21\u201326). Pointnet: Deep learning on point sets for 3d classification and segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA."},{"key":"ref_49","first-page":"20745","article-title":"Object dgcnn: 3d object detection using dynamic graphs","volume":"34","author":"Wang","year":"2021","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Chen, H., Liu, S., Chen, W., Li, H., and Hill, R. (2021, January 20\u201325). Equivariant point network for 3d point cloud analysis. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Nashville, TN, USA.","DOI":"10.1109\/CVPR46437.2021.01428"},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Komorowski, J., Wysoczan\u02d9ska, M., and Trzcinski, T. (2021, January 18\u201322). MinkLoc++: Lidar and monocular image fusion for place recognition. Proceedings of the 2021 International Joint Conference on Neural Networks (IJCNN), Shenzhen, China.","DOI":"10.1109\/IJCNN52387.2021.9533373"},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Kuk, J.G., An, J.H., Ki, H., and Cho, N.I. (2010, January 19\u201322). Fast lane detection &amp tracking based on Hough transform with reduced memory requirement. Proceedings of the 13th International IEEE Conference on Intelligent Transportation Systems, Funchal, Portugal.","DOI":"10.1109\/ITSC.2010.5625121"},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Chen, L., Wu, P., Chitta, K., Jaeger, B., Geiger, A., and Li, H. (2023). End-to-end autonomous driving: Challenges and frontiers. arXiv.","DOI":"10.1109\/TPAMI.2024.3435937"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"3228","DOI":"10.1109\/TITS.2023.3327949","article-title":"Robust Cognitive Capability in Autonomous Driving Using Sensor Fusion Techniques: A Survey","volume":"25","author":"Nawaz","year":"2023","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Wang, T.H., Maalouf, A., Xiao, W., Ban, Y., Amini, A., Rosman, G., Karaman, S., and Rus, D. (2023). Drive anywhere: Generalizable end-to-end autonomous driving with multi-modal foundation models. arXiv.","DOI":"10.1109\/ICRA57147.2024.10611590"},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Gao, Z., Mu, Y., Chen, C., Duan, J., Luo, P., Lu, Y., and Li, S.E. (2024). Enhance sample efficiency and robustness of end-to-end urban autonomous driving via semantic masked world model. IEEE Trans. Intell. Transp. Syst., 1\u201313.","DOI":"10.1109\/TITS.2024.3400227"},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"253","DOI":"10.1016\/bs.adcom.2023.04.006","article-title":"Irregular situations in real-world intelligent systems","volume":"Volume 134","author":"Mishra","year":"2024","journal-title":"Advances in Computers"},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Mishra, A., Kim, J., Cha, J., Kim, D., and Kim, S. (2021). Authorized traffic controller hand gesture recognition for situation-aware autonomous driving. Sensors, 21.","DOI":"10.3390\/s21237914"},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"1230","DOI":"10.23919\/cje.2022.00.093","article-title":"Towards V2I age-aware fairness access: A DQN based intelligent vehicular node training and test method","volume":"32","author":"Qiong","year":"2023","journal-title":"Chin. J. Electron."},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Joo, S.H., Manzoor, S., Rocha, Y.G., Bae, S.H., Lee, K.H., Kuc, T.Y., and Kim, M. (2020). Autonomous navigation framework for intelligent robots based on a semantic environment modeling. Appl. Sci., 10.","DOI":"10.3390\/app10093219"},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Pyo, J.W., Bae, S.H., Joo, S.H., Lee, M.K., Ghosh, A., and Kuc, T.Y. (2022). Development of an Autonomous Driving Vehicle for Garbage Collection in Residential Areas. Sensors, 22.","DOI":"10.3390\/s22239094"},{"key":"ref_62","unstructured":"Jocher, G., Chaurasia, A., and Qiu, J. (2024, July 31). Ultralytics YOLO (Version 8.0.0) [Computer Software]. Available online: https:\/\/github.com\/ultralytics\/ultralytics."},{"key":"ref_63","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_64","doi-asserted-by":"crossref","first-page":"2053","DOI":"10.1109\/TRO.2022.3141876","article-title":"Fast-lio2: Fast direct lidar-inertial odometry","volume":"38","author":"Xu","year":"2022","journal-title":"IEEE Trans. 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