{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,12]],"date-time":"2026-05-12T13:12:47Z","timestamp":1778591567182,"version":"3.51.4"},"reference-count":54,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2026,5,12]],"date-time":"2026-05-12T00:00:00Z","timestamp":1778544000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Defense Innovation Institution"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Robust LiDAR point cloud processing in dynamic outdoor environments, where movable objects such as vehicles and pedestrians introduce significant structural uncertainty, remains a key challenge for remote sensing and autonomous systems. This work addresses LiDAR-based place recognition (LPR), a critical component for loop closure and re-localization that is highly susceptible to such dynamics. While semantic information is beneficial, existing methods often require external segmentation models at inference or lack explicit mechanisms to suppress movable objects under uncertain predictions. To address these limitations, we propose SGMR-LPR, an end-to-end semantic-guided framework designed to explicitly counteract movable-object interference during feature encoding. Building on the \u201csegmentation-while-describing\u201d paradigm, SGMR-LPR incorporates an internal semantic segmentation branch and two novel modules: a probabilistic movable object masking (PMOM) module, which transforms semantic logits into continuous, uncertainty-aware masks of movable regions; and a movable-suppressed channel\u2013spatial attention (MSCS) module, which uses these masks to adaptively modulate high-level BEV features\u2014suppressing responses from movable-object regions while enhancing stable structural elements. By embedding explicit movable-awareness into feature modulation, SGMR-LPR achieves enhanced robustness without external semantic models at inference. Extensive experiments on multiple benchmarks demonstrate consistent performance gains, particularly in scenes with dense movable objects, advancing reliable point cloud-based scene understanding in dynamic environments.<\/jats:p>","DOI":"10.3390\/s26103050","type":"journal-article","created":{"date-parts":[[2026,5,12]],"date-time":"2026-05-12T12:33:37Z","timestamp":1778589217000},"page":"3050","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["SGMR-LPR: A Semantic-Guided Network Robust to Movable Objects for LiDAR-Based Place Recognition"],"prefix":"10.3390","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-3048-3704","authenticated-orcid":false,"given":"Weizhong","family":"Jiang","sequence":"first","affiliation":[{"name":"Defense Innovation Institute, Beijing 100071, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhipeng","family":"Xiao","sequence":"additional","affiliation":[{"name":"Defense Innovation Institute, Beijing 100071, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lilin","family":"Qian","sequence":"additional","affiliation":[{"name":"Defense Innovation Institute, Beijing 100071, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Erke","family":"Shang","sequence":"additional","affiliation":[{"name":"Defense Innovation Institute, Beijing 100071, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dawei","family":"Zhao","sequence":"additional","affiliation":[{"name":"Defense Innovation Institute, Beijing 100071, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qi","family":"Zhu","sequence":"additional","affiliation":[{"name":"Defense Innovation Institute, Beijing 100071, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6959-4343","authenticated-orcid":false,"given":"Liang","family":"Xiao","sequence":"additional","affiliation":[{"name":"Defense Innovation Institute, Beijing 100071, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2026,5,12]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1007\/s10462-024-10713-6","article-title":"3D point cloud-based place recognition: A survey","volume":"57","author":"Luo","year":"2024","journal-title":"Artif. Intell. Rev."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"363","DOI":"10.1007\/s10462-025-11367-8","article-title":"Place recognition meet multiple modalities: A comprehensive review, current challenges and future development","volume":"58","author":"Li","year":"2025","journal-title":"Artif. Intell. Rev."},{"key":"ref_3","first-page":"0003","article-title":"A survey on deep learning-based lidar place recognition","volume":"2","author":"Jiang","year":"2025","journal-title":"Artif. Intell. Auton. Syst."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"11249","DOI":"10.1109\/TITS.2021.3102429","article-title":"PSE-Match: A viewpoint-free place recognition method with parallel semantic embedding","volume":"23","author":"Yin","year":"2021","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1319","DOI":"10.1109\/LRA.2023.3239312","article-title":"PADLoC: LiDAR-based deep loop closure detection and registration using panoptic attention","volume":"8","author":"Arce","year":"2023","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Li, L., Kong, X., Zhao, X., Huang, T., Li, W., Wen, F., Zhang, H., and Liu, Y. (2021). SSC: Semantic scan context for large-scale place recognition. Proceedings of the 2021 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), IEEE.","DOI":"10.1109\/IROS51168.2021.9635904"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Kong, X., Yang, X., Zhai, G., Zhao, X., Zeng, X., Wang, M., Liu, Y., Li, W., and Wen, F. (2020). Semantic graph based place recognition for 3d point clouds. Proceedings of the 2020 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), IEEE.","DOI":"10.1109\/IROS45743.2020.9341060"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"4321","DOI":"10.1109\/LRA.2022.3150499","article-title":"RINet: Efficient 3D LiDAR-based place recognition using rotation invariant neural network","volume":"7","author":"Li","year":"2022","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"97","DOI":"10.1016\/j.isprsjprs.2024.03.002","article-title":"Semantics-enhanced discriminative descriptor learning for LiDAR-based place recognition","volume":"210","author":"Chen","year":"2024","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Jiang, W., Xue, H., Si, S., Min, C., Xiao, L., Nie, Y., and Dai, B. (2024). SG-LPR: Semantic-Guided LiDAR-Based Place Recognition. Electronics, 13.","DOI":"10.3390\/electronics13224532"},{"key":"ref_11","unstructured":"Shi, P., Zhang, Y., and Li, J. (2023). LiDAR-based place recognition for autonomous driving: A survey. arXiv."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"3139","DOI":"10.1007\/s11263-024-02019-5","article-title":"A survey on global lidar localization: Challenges, advances and open problems","volume":"132","author":"Yin","year":"2024","journal-title":"Int. J. Comput. Vis."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Kim, G., and Kim, A. (2018). Scan Context: Egocentric spatial descriptor for place recognition within 3d point cloud map. Proceedings of the 2018 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), IEEE.","DOI":"10.1109\/IROS.2018.8593953"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Wang, Y., Sun, Z., Xu, C.Z., Sarma, S.E., Yang, J., and Kong, H. (2020). LiDAR iris for loop-closure detection. Proceedings of the 2020 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), IEEE.","DOI":"10.1109\/IROS45743.2020.9341010"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"He, L., Wang, X., and Zhang, H. (2016). M2DP: A novel 3D point cloud descriptor and its application in loop closure detection. Proceedings of the 2016 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), IEEE.","DOI":"10.1109\/IROS.2016.7759060"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Magnusson, M., Andreasson, H., Nuchter, A., and Lilienthal, A.J. (2009). Appearance-based loop detection from 3D laser data using the normal distributions transform. Proceedings of the 2009 IEEE International Conference on Robotics and Automation, IEEE.","DOI":"10.1109\/ROBOT.2009.5152712"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Dub\u00e9, R., Dugas, D., Stumm, E., Nieto, J., Siegwart, R., and Cadena, C. (2017). Segmatch: Segment based place recognition in 3d point clouds. Proceedings of the 2017 IEEE International Conference on Robotics and Automation (ICRA), IEEE.","DOI":"10.1109\/ICRA.2017.7989618"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Bosse, M., and Zlot, R. (2013). Place recognition using keypoint voting in large 3D lidar datasets. Proceedings of the 2013 IEEE International Conference on Robotics and Automation, IEEE.","DOI":"10.1109\/ICRA.2013.6630945"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Uy, M.A., and Lee, G.H. (2018). PointNetVLAD: Deep point cloud based retrieval for large-scale place recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), IEEE.","DOI":"10.1109\/CVPR.2018.00470"},{"key":"ref_20","unstructured":"Qi, C.R., Su, H., Mo, K., and Guibas, L.J. (2017). Pointnet: Deep learning on point sets for 3d classification and segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, IEEE."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Zhang, W., and Xiao, C. (2019). PCAN: 3D attention map learning using contextual information for point cloud based retrieval. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, IEEE.","DOI":"10.1109\/CVPR.2019.01272"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Xia, Y., Xu, Y., Li, S., Wang, R., Du, J., Cremers, D., and Stilla, U. (2021). SOE-Net: A self-attention and orientation encoding network for point cloud based place recognition. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, IEEE.","DOI":"10.1109\/CVPR46437.2021.01119"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Sun, Q., Liu, H., He, J., Fan, Z., and Du, X. (2020). Dagc: Employing dual attention and graph convolution for point cloud based place recognition. Proceedings of the 2020 International Conference on Multimedia Retrieval (ICMR 2020), Dublin, Ireland, 8\u201311 June 2020, Association for Computing Machinery.","DOI":"10.1145\/3372278.3390693"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Hui, L., Yang, H., Cheng, M., Xie, J., and Yang, J. (2021). Pyramid point cloud transformer for large-scale place recognition. Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV), IEEE.","DOI":"10.1109\/ICCV48922.2021.00604"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Komorowski, J. (2021). Minkloc3d: Point cloud based large-scale place recognition. Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision (WACV), IEEE.","DOI":"10.1109\/WACV48630.2021.00183"},{"key":"ref_26","first-page":"551","article-title":"SVT-Net: Super light-weight sparse voxel transformer for large scale place recognition","volume":"Volume 36","author":"Fan","year":"2022","journal-title":"Proceedings of the AAAI Conference on Artificial Intelligence (AAAI), Virtual Conference, 22 February\u20131 March 2022"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Wu, T., Fu, H., Liu, B., Xue, H., Ren, R., and Tu, Z. (2021). Detailed analysis on generating the range image for lidar point cloud processing. Electronics, 10.","DOI":"10.3390\/electronics10111224"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Chen, X., L\u00e4be, T., Milioto, A., R\u00f6hling, T., Vysotska, O., Haag, A., Behley, J., and Stachniss, C. (2021). OverlapNet: Loop closing for LiDAR-based SLAM. arXiv.","DOI":"10.15607\/RSS.2020.XVI.009"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"6958","DOI":"10.1109\/LRA.2022.3178797","article-title":"OverlapTransformer: An efficient and yaw-angle-invariant transformer network for LiDAR-based place recognition","volume":"7","author":"Ma","year":"2022","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Luo, L., Zheng, S., Li, Y., Fan, Y., Yu, B., Cao, S.Y., Li, J., and Shen, H.L. (2023). BEVPlace: Learning LiDAR-based place recognition using bird\u2019s eye view images. Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV), IEEE.","DOI":"10.1109\/ICCV51070.2023.00799"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"2791","DOI":"10.1109\/LRA.2021.3060741","article-title":"Disco: Differentiable scan context with orientation","volume":"6","author":"Xu","year":"2021","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"4039","DOI":"10.1109\/TII.2023.3313635","article-title":"CVTNet: A cross-view transformer network for LiDAR-based place recognition in autonomous driving environments","volume":"20","author":"Ma","year":"2023","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"17174","DOI":"10.1109\/TITS.2024.3421375","article-title":"MVSE-Net: A Multi-View Deep Network with Semantic Embedding for LiDAR Place Recognition","volume":"25","author":"Zhang","year":"2024","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Luo, L., Cao, S., Li, X., Xu, J., Ai, R., Yu, Z., and Chen, X. (2024). BEVPlace++: Fast, Robust, and Lightweight LiDAR Global Localization for Unmanned Ground Vehicles. arXiv.","DOI":"10.1109\/TRO.2025.3585385"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Vidanapathirana, K., Moghadam, P., Harwood, B., Zhao, M., Sridharan, S., and Fookes, C. (2021). Locus: LiDAR-based place recognition using spatiotemporal higher-order pooling. Proceedings of the 2021 IEEE International Conference on Robotics and Automation (ICRA), IEEE.","DOI":"10.1109\/ICRA48506.2021.9560915"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"2145","DOI":"10.1109\/TIP.2024.3364511","article-title":"SC_LPR: Semantically consistent LiDAR place recognition based on chained cascade network in long-term dynamic environments","volume":"33","author":"Kong","year":"2024","journal-title":"IEEE Trans. Image Process."},{"key":"ref_37","unstructured":"Schorghuber, M., Steininger, D., Cabon, Y., Humenberger, M., and Gelautz, M. (November, January 27). SLAMANTIC\u2014Leveraging Semantics to Improve VSLAM in Dynamic Environments. Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV) Workshops, Seoul, Republic of Korea."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Yu, C., Liu, Z., Liu, X., Xie, F., Yang, Y., Wei, Q., and Fei, Q. (2018). DS-SLAM: A Semantic Visual SLAM towards Dynamic Environments. Proceedings of the 2018 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), IEEE.","DOI":"10.1109\/IROS.2018.8593691"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"8507810","DOI":"10.1109\/TIM.2025.3561381","article-title":"TRLO: An Efficient LiDAR Odometry with 3-D Dynamic Object Tracking and Removal","volume":"74","author":"Jia","year":"2025","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Yuan, Z., Wang, X., Wu, J., Cheng, J., and Yang, X. (2024). LiDAR-Inertial Odometry in Dynamic Driving Scenarios using Label Consistency Detection. arXiv.","DOI":"10.1109\/IROS60139.2025.11246976"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"12636","DOI":"10.1109\/LRA.2025.3623436","article-title":"Breaking the Static Assumption: A Dynamic-Aware LIO Framework via Spatio-Temporal Normal Analysis","volume":"10","author":"Chen","year":"2025","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1016\/j.isprsjprs.2023.11.013","article-title":"An efficient point cloud place recognition approach based on transformer in dynamic environment","volume":"207","author":"Li","year":"2024","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Cao, H., Wang, Y., Chen, J., Jiang, D., Zhang, X., Tian, Q., and Wang, M. (2022). Swin-Unet: Unet-like pure transformer for medical image segmentation. Proceedings of the European Conference on Computer Vision (ECCV), Springer.","DOI":"10.1007\/978-3-031-25066-8_9"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Behley, J., Garbade, M., Milioto, A., Quenzel, J., Behnke, S., Stachniss, C., and Gall, J. (2019). SemanticKITTI: A dataset for semantic scene understanding of lidar sequences. Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV), IEEE.","DOI":"10.1109\/ICCV.2019.00939"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., and Guo, B. (2021). Swin Transformer: Hierarchical vision transformer using shifted windows. Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV), IEEE.","DOI":"10.1109\/ICCV48922.2021.00986"},{"key":"ref_46","first-page":"3","article-title":"Cbam: Convolutional block attention module","volume":"Volume 11211","author":"Woo","year":"2018","journal-title":"Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany, 8\u201314 September 2018"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Arandjelovic, R., Gronat, P., Torii, A., Pajdla, T., and Sivic, J. (2016). NetVLAD: CNN architecture for weakly supervised place recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), IEEE.","DOI":"10.1109\/CVPR.2016.572"},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Geiger, A., Lenz, P., and Urtasun, R. (2012). Are we ready for autonomous driving? the kitti vision benchmark suite. Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), IEEE.","DOI":"10.1109\/CVPR.2012.6248074"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"3292","DOI":"10.1109\/TPAMI.2022.3179507","article-title":"Kitti-360: A novel dataset and benchmarks for urban scene understanding in 2d and 3d","volume":"45","author":"Liao","year":"2022","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"1023","DOI":"10.1177\/0278364915614638","article-title":"University of Michigan North Campus long-term vision and lidar dataset","volume":"35","author":"Ushani","year":"2016","journal-title":"Int. J. Robot. Res."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Jiang, W., Xue, H., Si, S., Xiao, L., Zhao, D., Zhu, Q., Nie, Y., and Dai, B. (2025). R2SCAT-LPR: Rotation-Robust Network with Self-and Cross-Attention Transformers for LiDAR-Based Place Recognition. Remote Sens., 17.","DOI":"10.3390\/rs17061057"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"2828","DOI":"10.1109\/LRA.2022.3221336","article-title":"Bow3d: Bag of words for real-time loop closing in 3d lidar slam","volume":"8","author":"Cui","year":"2022","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Vidanapathirana, K., Ramezani, M., Moghadam, P., Sridharan, S., and Fookes, C. (2022). LoGG3D-Net: Locally guided global descriptor learning for 3D place recognition. Proceedings of the 2022 International Conference on Robotics and Automation (ICRA), IEEE.","DOI":"10.1109\/ICRA46639.2022.9811753"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"2074","DOI":"10.1109\/TRO.2022.3150683","article-title":"Lcdnet: Deep loop closure detection and point cloud registration for lidar slam","volume":"38","author":"Cattaneo","year":"2022","journal-title":"IEEE Trans. Robot."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/10\/3050\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,12]],"date-time":"2026-05-12T12:43:30Z","timestamp":1778589810000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/10\/3050"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,5,12]]},"references-count":54,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2026,5]]}},"alternative-id":["s26103050"],"URL":"https:\/\/doi.org\/10.3390\/s26103050","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,5,12]]}}}