{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,28]],"date-time":"2026-09-28T16:56:51Z","timestamp":1790614611551,"version":"4.1.0"},"reference-count":47,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2026,9,26]],"date-time":"2026-09-26T00:00:00Z","timestamp":1790380800000},"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>Active robotic target observation in occluded manufacturing workspaces is difficult when unreliable stereo depth, missing obstacle-height information, and infeasible viewpoint commands propagate through the perception\u2013mapping\u2013planning loop. This study develops a reliability-gated stereo vision and 2.5D fusion framework for active target observation with an eye-in-hand stereo system. The framework screens low-quality stereo observations before fusion, verifies whether accepted observations produce effective map updates, and maintains a 2.5D occupancy-height representation for height-aware viewpoint generation. Using this representation, constrained semantic\u2013geometric ranking selects next-best-view (NBV) candidates, while execution feedback, failed-region avoidance, and anchor recovery maintain closed-loop continuity. Closed-loop simulations are conducted in occluded robotic workcell scenarios with different difficulty levels. The method achieves 100% success in all Easy and Medium scenes. In Medium2, success rates are 80% for the 2D baseline and 100% for the 2.5D representation; the corresponding mean search steps among successful trials are 6.12 \u00b1 0.64 and 3.40 \u00b1 0.70, respectively. Real-scene stereo front-end validation supports depth recovery, quality-gated observation screening, initial 2.5D map fusion, and first-NBV generation under occlusion. These results indicate that reliability-gated stereo fusion and height-aware viewpoint planning can improve active robotic observation in occluded manufacturing workspaces.<\/jats:p>","DOI":"10.3390\/s26196106","type":"journal-article","created":{"date-parts":[[2026,9,28]],"date-time":"2026-09-28T15:57:57Z","timestamp":1790611077000},"page":"6106","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Reliability-Gated Stereo Vision and 2.5D Sensor Fusion for Active Robotic Observation in Occluded Manufacturing Workspaces"],"prefix":"10.3390","volume":"26","author":[{"given":"Mengjie","family":"Li","sequence":"first","affiliation":[{"name":"School of Electronic Information and Electrical Engineering, Yangtze University, Jingzhou 434023, China"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"conceptualization","vocabulary":"credit"},{"role":"methodology","vocabulary":"credit"},{"role":"software","vocabulary":"credit"},{"role":"validation","vocabulary":"credit"},{"role":"formal-analysis","vocabulary":"credit"},{"role":"investigation","vocabulary":"credit"},{"role":"data-curation","vocabulary":"credit"},{"role":"writing-original-draft","vocabulary":"credit"},{"role":"visualization","vocabulary":"credit"}]},{"given":"Le","family":"Zhao","sequence":"additional","affiliation":[{"name":"School of Electronic Information and Electrical Engineering, Yangtze University, Jingzhou 434023, China"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"methodology","vocabulary":"credit"},{"role":"software","vocabulary":"credit"},{"role":"validation","vocabulary":"credit"},{"role":"writing-review-editing","vocabulary":"credit"}]},{"given":"Leqi","family":"Li","sequence":"additional","affiliation":[{"name":"School of Electronic Information and Electrical Engineering, Yangtze University, Jingzhou 434023, China"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"methodology","vocabulary":"credit"},{"role":"writing-review-editing","vocabulary":"credit"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-8922-6219","authenticated-orcid":false,"given":"Yuxiao","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Computer Science, Yangtze University, Jingzhou 434023, China"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"writing-review-editing","vocabulary":"credit"},{"role":"visualization","vocabulary":"credit"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6175-4100","authenticated-orcid":false,"given":"Gengpei","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Electronic Information and Electrical Engineering, Yangtze University, Jingzhou 434023, China"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"conceptualization","vocabulary":"credit"},{"role":"resources","vocabulary":"credit"},{"role":"writing-review-editing","vocabulary":"credit"},{"role":"supervision","vocabulary":"credit"},{"role":"project-administration","vocabulary":"credit"}]}],"member":"1968","published-online":{"date-parts":[[2026,9,26]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"100098","DOI":"10.1016\/j.ophoto.2025.100098","article-title":"A structured review and taxonomy of next-best-view strategies for 3D reconstruction","volume":"17","author":"Alsadik","year":"2025","journal-title":"ISPRS Open J. Photogramm. Remote Sens."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"116672","DOI":"10.1016\/j.measurement.2025.116672","article-title":"A two-stage fast stereo matching algorithm for real-time 3D coordinate computation","volume":"247","author":"Liu","year":"2025","journal-title":"Measurement"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"115699","DOI":"10.1016\/j.measurement.2024.115699","article-title":"Underwater measurement of abalone shell\u2019s size based on stereo vision","volume":"242","author":"Chen","year":"2025","journal-title":"Measurement"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"114886","DOI":"10.1016\/j.measurement.2024.114886","article-title":"Highly accurate three-dimensional measurement of large structures using multiple stereo vision with improved two-step calibration algorithm","volume":"234","author":"Kim","year":"2024","journal-title":"Measurement"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"11047","DOI":"10.1109\/TPAMI.2024.3451994","article-title":"Unsupervised active visual search with Monte Carlo planning under uncertain detections","volume":"46","author":"Taioli","year":"2024","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"966","DOI":"10.1109\/5.5968","article-title":"Active perception","volume":"76","author":"Bajcsy","year":"1988","journal-title":"Proc. IEEE"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"177","DOI":"10.1007\/s10514-017-9615-3","article-title":"Revisiting active perception","volume":"42","author":"Bajcsy","year":"2018","journal-title":"Auton. Robot."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"225","DOI":"10.1007\/s41095-020-0179-3","article-title":"View planning in robot active vision: A survey of systems, algorithms, and applications","volume":"6","author":"Zeng","year":"2020","journal-title":"Comput. Vis. Media"},{"key":"ref_9","unstructured":"Yamauchi, B. (1997, January 10\u201311). A frontier-based approach for autonomous exploration. Proceedings of the 1997 IEEE International Symposium on Computational Intelligence in Robotics and Automation (CIRA\u201997), Monterey, CA, USA."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Bircher, A., Kamel, M., Alexis, K., Oleynikova, H., and Siegwart, R. (2016, January 16\u201321). Receding horizon \u201cnext-best-view\u201d planner for 3D exploration. Proceedings of the 2016 IEEE International Conference on Robotics and Automation (ICRA), Stockholm, Sweden.","DOI":"10.1109\/ICRA.2016.7487281"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Zhu, H., Cao, C., Xia, Y., Scherer, S., Zhang, J., and Wang, W. (October, January 27). DSVP: Dual-stage viewpoint planner for rapid exploration by dynamic expansion. Proceedings of the 2021 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), Prague, Czech Republic.","DOI":"10.1109\/IROS51168.2021.9636473"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Cao, C., Zhu, H., Choset, H., and Zhang, J. (2021, January 12\u201316). TARE: A hierarchical framework for efficiently exploring complex 3D environments. Proceedings of the Robotics: Science and Systems XVII, Virtual.","DOI":"10.15607\/RSS.2021.XVII.018"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Vasquez-Gomez, J.I., Sucar, L.E., and Murrieta-Cid, R. (2014, January 14\u201318). View planning for 3D object reconstruction with a mobile manipulator robot. Proceedings of the 2014 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), Chicago, IL, USA.","DOI":"10.1109\/IROS.2014.6943158"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"3779","DOI":"10.1109\/LRA.2022.3146558","article-title":"Online next-best-view planner for 3D-exploration and inspection with a mobile manipulator robot","volume":"7","author":"Naazare","year":"2022","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1109\/34.745736","article-title":"Active vision for complete scene reconstruction and exploration","volume":"21","author":"Marchand","year":"1999","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1540","DOI":"10.1109\/LRA.2017.2660769","article-title":"An adaptable, probabilistic, next-best view algorithm for reconstruction of unknown 3-D objects","volume":"2","author":"Daudelin","year":"2017","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"615","DOI":"10.1016\/j.robot.2007.10.006","article-title":"Path planning for laser scanning with an industrial robot","volume":"56","author":"Larsson","year":"2008","journal-title":"Robot. Auton. Syst."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Radmard, S., Meger, D., Croft, E.A., and Little, J.J. (2013, January 6\u201310). Overcoming unknown occlusions in eye-in-hand visual search. Proceedings of the 2013 IEEE International Conference on Robotics and Automation (ICRA), Karlsruhe, Germany.","DOI":"10.1109\/ICRA.2013.6631004"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Li, J.K., Hsu, D., and Lee, W.S. (2016, January 9\u201314). Act to see and see to act: POMDP planning for object search in clutter. Proceedings of the 2016 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), Daejeon, Republic of Korea.","DOI":"10.1109\/IROS.2016.7759839"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Novkovic, T., Pautrat, R., Furrer, F., Breyer, M., Siegwart, R., and Nieto, J. (August, January 31). Object finding in cluttered scenes using interactive perception. Proceedings of the 2020 IEEE International Conference on Robotics and Automation (ICRA), Paris, France.","DOI":"10.1109\/ICRA40945.2020.9197101"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"46","DOI":"10.1109\/2.30720","article-title":"Using occupancy grids for mobile robot perception and navigation","volume":"22","author":"Elfes","year":"1989","journal-title":"Computer"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"189","DOI":"10.1007\/s10514-012-9321-0","article-title":"OctoMap: An efficient probabilistic 3D mapping framework based on octrees","volume":"34","author":"Hornung","year":"2013","journal-title":"Auton. Robot."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Curless, B., and Levoy, M. (1996). A volumetric method for building complex models from range images. Proceedings of the 23rd Annual Conference on Computer Graphics and Interactive Techniques (SIGGRAPH \u201996), New Orleans, LA, USA, 4\u20139 August 1996, ACM.","DOI":"10.1145\/237170.237269"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Newcombe, R.A., Fitzgibbon, A., Izadi, S., Hilliges, O., Molyneaux, D., Kim, D., Davison, A.J., Kohli, P., Shotton, J., and Hodges, S. (2011, January 26\u201329). KinectFusion: Real-time dense surface mapping and tracking. Proceedings of the 2011 10th IEEE International Symposium on Mixed and Augmented Reality (ISMAR), Basel, Switzerland.","DOI":"10.1109\/ISMAR.2011.6092378"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Vizzo, I., Guadagnino, T., Behley, J., and Stachniss, C. (2022). VDBFusion: Flexible and efficient TSDF integration of range sensor data. Sensors, 22.","DOI":"10.3390\/s22031296"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Oleynikova, H., Taylor, Z., Fehr, M., Siegwart, R., and Nieto, J. (2017, January 24\u201328). Voxblox: Incremental 3D Euclidean signed distance fields for on-board MAV planning. Proceedings of the 2017 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), Vancouver, BC, Canada.","DOI":"10.1109\/IROS.2017.8202315"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Miki, T., Wellhausen, L., Grandia, R., Jenelten, F., Homberger, T., and Hutter, M. (2022, January 23\u201327). Elevation mapping for locomotion and navigation using GPU. Proceedings of the 2022 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), Kyoto, Japan.","DOI":"10.1109\/IROS47612.2022.9981507"},{"key":"ref_28","unstructured":"Tokhi, M.O., Koz\u0142owski, K., and Virk, G.S. (2014). Robot-centric elevation mapping with uncertainty estimates. Mobile Service Robotics, World Scientific."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1738","DOI":"10.1109\/TPAMI.2020.3032602","article-title":"A survey on deep learning techniques for stereo-based depth estimation","volume":"44","author":"Laga","year":"2022","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Lipson, L., Teed, Z., and Deng, J. (2021, January 1\u20133). RAFT-Stereo: Multilevel recurrent field transforms for stereo matching. Proceedings of the 2021 International Conference on 3D Vision (3DV), London, UK.","DOI":"10.1109\/3DV53792.2021.00032"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"70","DOI":"10.1016\/j.imavis.2017.02.005","article-title":"Estimation of measurement uncertainty in stereo vision system","volume":"61","author":"Sankowski","year":"2017","journal-title":"Image Vis. Comput."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"323","DOI":"10.1007\/978-3-030-01231-1_20","article-title":"Beyond local reasoning for stereo confidence estimation with deep learning","volume":"Volume 11210","author":"Ferrari","year":"2018","journal-title":"Computer Vision\u2014ECCV 2018"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"102905","DOI":"10.1016\/j.cviu.2020.102905","article-title":"Learning a confidence measure in the disparity domain from O(1) features","volume":"193","author":"Poggi","year":"2020","journal-title":"Comput. Vis. Image Underst."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"715","DOI":"10.1007\/978-3-030-58586-0_42","article-title":"Self-adapting confidence estimation for stereo","volume":"Volume 12369","author":"Vedaldi","year":"2020","journal-title":"Computer Vision\u2014ECCV 2020"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Zeng, Z., R\u00f6fer, A., and Jenkins, O.C. (August, January 31). Semantic linking maps for active visual object search. Proceedings of the 2020 IEEE International Conference on Robotics and Automation (ICRA), Paris, France.","DOI":"10.1109\/ICRA40945.2020.9196830"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"986","DOI":"10.1109\/TRO.2013.2256686","article-title":"Active visual object search in unknown environments using uncertain semantics","volume":"29","author":"Aydemir","year":"2013","journal-title":"IEEE Trans. Robot."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Zheng, K., Chitnis, R., Sung, Y., Konidaris, G., and Tellex, S. (2022, January 23\u201327). Towards optimal correlational object search. Proceedings of the 2022 IEEE International Conference on Robotics and Automation (ICRA), Philadelphia, PA, USA.","DOI":"10.1109\/ICRA46639.2022.9812252"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Kunze, L., Doreswamy, K.K., and Hawes, N. (June, January 31). Using qualitative spatial relations for indirect object search. Proceedings of the 2014 IEEE International Conference on Robotics and Automation (ICRA), Hong Kong, China.","DOI":"10.1109\/ICRA.2014.6906604"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Zhu, Y., Mottaghi, R., Kolve, E., Lim, J.J., Gupta, A., Fei-Fei, L., and Farhadi, A. (June, January 29). Target-driven visual navigation in indoor scenes using deep reinforcement learning. Proceedings of the 2017 IEEE International Conference on Robotics and Automation (ICRA), Singapore.","DOI":"10.1109\/ICRA.2017.7989381"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Park, J., Yoon, T., Hong, J., Yu, Y., Pan, M., and Choi, S. (June, January 29). Zero-shot active visual search (ZAVIS): Intelligent object search for robotic assistants. Proceedings of the 2023 IEEE International Conference on Robotics and Automation (ICRA), London, UK.","DOI":"10.1109\/ICRA48891.2023.10161345"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Kunze, L., Beetz, M., Saito, M., Azuma, H., Okada, K., and Inaba, M. (2012, January 14\u201318). Searching objects in large-scale indoor environments: A decision-theoretic approach. Proceedings of the 2012 IEEE International Conference on Robotics and Automation (ICRA), Saint Paul, MN, USA.","DOI":"10.1109\/ICRA.2012.6224965"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"1093","DOI":"10.1016\/j.robot.2012.06.001","article-title":"Topological spatial relations for active visual search","volume":"60","author":"Aydemir","year":"2012","journal-title":"Robot. Auton. Syst."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"503","DOI":"10.1016\/j.robot.2008.03.008","article-title":"Curious George: An attentive semantic robot","volume":"56","author":"Meger","year":"2008","journal-title":"Robot. Auton. Syst."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"381","DOI":"10.1145\/358669.358692","article-title":"Random sample consensus: A paradigm for model fitting with applications to image analysis and automated cartography","volume":"24","author":"Fischler","year":"1981","journal-title":"Commun. ACM"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Rohmer, E., Singh, S.P.N., and Freese, M. (2013, January 3\u20137). V-REP: A versatile and scalable robot simulation framework. Proceedings of the 2013 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), Tokyo, Japan.","DOI":"10.1109\/IROS.2013.6696520"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Redmon, J., Divvala, S., Girshick, R., and Farhadi, A. (2016, January 27\u201330). You only look once: Unified, real-time object detection. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.91"},{"key":"ref_47","unstructured":"Ultralytics (2026, April 23). YOLOv8, Available online: https:\/\/github.com\/ultralytics\/ultralytics."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/19\/6106\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,28]],"date-time":"2026-09-28T16:00:18Z","timestamp":1790611218000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/19\/6106"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9,26]]},"references-count":47,"journal-issue":{"issue":"19","published-online":{"date-parts":[[2026,10]]}},"alternative-id":["s26196106"],"URL":"https:\/\/doi.org\/10.3390\/s26196106","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,9,26]]}}}