{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,19]],"date-time":"2026-06-19T22:40:39Z","timestamp":1781908839573,"version":"3.54.5"},"reference-count":47,"publisher":"MDPI AG","issue":"13","license":[{"start":{"date-parts":[[2021,7,4]],"date-time":"2021-07-04T00:00:00Z","timestamp":1625356800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Ministry of Trade, Industry and Energy under Robot Industrial Core Technology Develop-ment","award":["K_G012000921401"],"award-info":[{"award-number":["K_G012000921401"]}]},{"name":"National Research Foundation of Korea funded by the Ministry of Education","award":["2019R1A2C2010195"],"award-info":[{"award-number":["2019R1A2C2010195"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Most indoor environments have wheelchair adaptations or ramps, providing an opportunity for mobile robots to navigate sloped areas avoiding steps. These indoor environments with integrated sloped areas are divided into different levels. The multi-level areas represent a challenge for mobile robot navigation due to the sudden change in reference sensors as visual, inertial, or laser scan instruments. Using multiple cooperative robots is advantageous for mapping and localization since they permit rapid exploration of the environment and provide higher redundancy than using a single robot. This study proposes a multi-robot localization using two robots (leader and follower) to perform a fast and robust environment exploration on multi-level areas. The leader robot is equipped with a 3D LIDAR for 2.5D mapping and a Kinect camera for RGB image acquisition. Using 3D LIDAR, the leader robot obtains information for particle localization, with particles sampled from the walls and obstacle tangents. We employ a convolutional neural network on the RGB images for multi-level area detection. Once the leader robot detects a multi-level area, it generates a path and sends a notification to the follower robot to go into the detected location. The follower robot utilizes a 2D LIDAR to explore the boundaries of the even areas and generate a 2D map using an extension of the iterative closest point. The 2D map is utilized as a re-localization resource in case of failure of the leader robot.<\/jats:p>","DOI":"10.3390\/s21134588","type":"journal-article","created":{"date-parts":[[2021,7,4]],"date-time":"2021-07-04T22:35:22Z","timestamp":1625438122000},"page":"4588","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Multi-Robot 2.5D Localization and Mapping Using a Monte Carlo Algorithm on a Multi-Level Surface"],"prefix":"10.3390","volume":"21","author":[{"given":"Vinicio Alejandro","family":"Rosas-Cervantes","sequence":"first","affiliation":[{"name":"Mechanical Engineering Department, Kyung Hee University, Yongin 17104, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Quoc-Dong","family":"Hoang","sequence":"additional","affiliation":[{"name":"Mechanical Engineering Department, Kyung Hee University, Yongin 17104, Korea"},{"name":"Integrated Education Institute for Frontier Science and Technology (BK21 Four), Kyung Hee University, Yongin 17104, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4588-6736","authenticated-orcid":false,"given":"Soon-Geul","family":"Lee","sequence":"additional","affiliation":[{"name":"Mechanical Engineering Department, Kyung Hee University, Yongin 17104, Korea"},{"name":"Integrated Education Institute for Frontier Science and Technology (BK21 Four), Kyung Hee University, Yongin 17104, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jae-Hwan","family":"Choi","sequence":"additional","affiliation":[{"name":"Mechanical Engineering Department, Kyung Hee University, Yongin 17104, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,7,4]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2955","DOI":"10.1007\/s12555-019-0313-0","article-title":"3D Localization of a Mobile Robot by Using Monte Carlo Algorithm and 2D Features of 3D Point Cloud","volume":"18","author":"Lee","year":"2020","journal-title":"Int. J. Control. Autom. Syst."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Sakai, T., Koide, K., Miura, J., and Oishi, S. (2017, January 11\u201314). Large-scale 3D outdoor mapping and on-line localization using 3D-2D matching. Proceedings of the 2017 IEEE\/SICE International Symposium on System Integration (SII), Taipei, Taiwan.","DOI":"10.1109\/SII.2017.8279325"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"163","DOI":"10.1016\/j.robot.2011.01.002","article-title":"Distributed consensus algorithms for merging feature-based maps with limited communication","volume":"59","author":"Cortes","year":"2011","journal-title":"Robot. Auton. Syst."},{"key":"ref_4","unstructured":"Burgard, W., Moors, M., Fox, D., Simmons, R., and Thrun, S. (2000, January 24\u201328). Collaborative multi-robot exploration in Proceedings 2000 ICRA. Millennium Conference. Proceedings of the IEEE International Conference on Robotics and Automation. Symposia Proceedings (Cat. No.00CH37065), San Francisco, CA, USA."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Zhou, X.S., and Roumeliotis, S.I. (2006, January 9\u201315). Multi-robot SLAM with Unknown Initial Correspondence: The Robot Rendezvous Case. Proceedings of the 2006 IEEE\/RSJ International Conference on Intelligent Robots and Systems, Beijing, China.","DOI":"10.1109\/IROS.2006.282219"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"107193","DOI":"10.1016\/j.patcog.2019.107193","article-title":"UcoSLAM: Simultaneous localization and mapping by fusion of keypoints and squared planar markers","volume":"101","year":"2020","journal-title":"Pattern Recognit."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Zhang, J., and Singh, S. (2014, January 12\u201316). LOAM: Lidar Odometry and Mapping in Real-Time. Proceedings of the Robotics: Science and Systems (RSS \u201814), Berkeley, CA, USA.","DOI":"10.15607\/RSS.2014.X.007"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1019","DOI":"10.1016\/S0031-3203(98)80010-1","article-title":"New Algorithms for 2D and 3D Point Matching: Pose Estimation and Correspondence","volume":"31","author":"Gold","year":"1994","journal-title":"Pattern Recognit."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Tsin, Y., and Kanade, T. (2004). A Correlation-Based Approach to Robust Point Set Registration. Computer Vision\u2014ECCV 2004, Springer.","DOI":"10.1007\/978-3-540-24672-5_44"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"221","DOI":"10.1023\/B:VISI.0000011205.11775.fd","article-title":"Lucas-Kanade 20 Years On: A Unifying Framework","volume":"56","author":"Baker","year":"2004","journal-title":"Int. J. Comput. Vis."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Zhang, Y., and Xiao, Y. (2011, January 10\u201315). Notice of Retraction: A patrolling scheme in wireless sensor and robot networks. Proceedings of the 2011 IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS), Shanghai, China.","DOI":"10.1109\/INFCOMW.2011.5928867"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Yan, C., and Zhang, T. (2016). Multi-robot patrol: A distributed algorithm based on expected idleness. Int. J. Adv. Robot. Syst., 13.","DOI":"10.1177\/1729881416663666"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"305","DOI":"10.1007\/s10514-008-9097-4","article-title":"Fast and accurate map merging for multi-robot systems","volume":"25","author":"Carpin","year":"2008","journal-title":"Auton. Robot."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"274","DOI":"10.1177\/0278364916687027","article-title":"Constructing informative Bayesian map priors: A multi-objective optimisation approach applied to indoor occupancy grid mapping","volume":"36","author":"Georgiou","year":"2017","journal-title":"Int. J. Robot. Res."},{"key":"ref_15","first-page":"53","article-title":"Improving SLAM by Exploiting Building Information from Publicly Available Maps and Localization Priors","volume":"85","author":"Vysotska","year":"2017","journal-title":"PFG\u2014J. Photogramm. Remote. Sens. Geoinf. Sci."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Mielle, M., Magnusson, M., Andreasson, H., and Lilienthal, A. (2017, January 11\u201313). SLAM auto-complete: Completing a robot map using an emergency map. Proceedings of the 2017 IEEE International Symposium on Safety, Security and Rescue Robotics (SSRR), Shanghai, China.","DOI":"10.1109\/SSRR.2017.8088137"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"601","DOI":"10.1177\/0278364905056348","article-title":"Topological Map Merging","volume":"24","author":"Huang","year":"2005","journal-title":"Int. J. Robot. Res."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Mielle, M., Magnusson, M., and Lilienthal, A.J. (2016, January 23\u201327). Using sketch-maps for robot navigation: Interpretation and matching. Proceedings of the 2016 IEEE International Symposium on Safety, Security and Rescue Robotics (SSRR), Lausanne, Switzerland.","DOI":"10.1109\/SSRR.2016.7784307"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Pippin, C., Christensen, H., and Weiss, L. (2013, January 18\u201322). Performance based task assignment in multi-robot patrolling. Proceedings of the 28th Annual ACM Symposium on Applied Computing, Coimbra, Portugal.","DOI":"10.1145\/2480362.2480378"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Kakuma, D., Tsuichihara, S., Ricardez, G.A.G., Takamatsu, J., and Ogasawara, T. (February, January 30). Alignment of Occupancy Grid and Floor Maps Using Graph Matching. Proceedings of the 2017 IEEE 11th International Conference on Semantic Computing (ICSC), San Diego, CA, USA.","DOI":"10.1109\/ICSC.2017.38"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Krajn\u00edk, T., Fentanes, J.P., Hanheide, M., and Duckett, T. (2016, January 9\u201314). Persistent localization and life-long mapping in changing environments using the Frequency Map Enhancement. Proceedings of the 2016 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), Daejeon, Korea.","DOI":"10.1109\/IROS.2016.7759671"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Pfingsthorn, M., and Birk, A. (2008, January 22\u201326). Efficiently communicating map updates with the pose graph. Proceedings of the 2008 IEEE\/RSJ International Conference on Intelligent Robots and Systems, Nice, France.","DOI":"10.1109\/IROS.2008.4651182"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"229","DOI":"10.1109\/70.938381","article-title":"A solution to the simultaneous localization and map building (SLAM) problem","volume":"17","author":"Dissanayake","year":"2001","journal-title":"IEEE Trans. Robot. Autom."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"435","DOI":"10.1007\/s10846-017-0710-7","article-title":"Autonomous Exploration with Exact Inverse Sensor Models","volume":"92","author":"Kaufman","year":"2018","journal-title":"J. Intell. Robot. Syst."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1007\/s10846-014-0093-y","article-title":"Pose Uncertainty in Occupancy Grids through Monte Carlo Integration","volume":"77","author":"Joubert","year":"2015","journal-title":"J. Intell. Robot. Syst."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Jadidi, M.G., Miro, J.V., and Dissanayake, G. (October, January 28). Mutual information-based exploration on continuous occupancy maps. Proceedings of the 2015 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), Hamburg, Germany.","DOI":"10.1109\/IROS.2015.7354244"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Eckart, B., Kim, K., Troccoli, A., Kelly, A., and Kautz, J. (2016, January 27\u201330). Accelerated Generative Models for 3D Point Cloud Data. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.593"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Souza, A., Maia, R., and Gon\u00e7alves, L. (2012). 3D Probabilistic Occupancy Grid to Robotic Mapping with Stereo Vision. Current Advancements in Stereo Vision, IntechOpen.","DOI":"10.1109\/LARS.2013.56"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Kaufman, E., Lee, T., and Ai, Z. (2016, January 13\u201316). Autonomous exploration by expected information gain from probabilistic occupancy grid mapping. Proceedings of the 2016 IEEE International Conference on Simulation, Modeling, and Programming for Autonomous Robots (SIMPAR), San Francisco, CA, USA.","DOI":"10.1109\/SIMPAR.2016.7862403"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Kaufman, E., Takami, K., Ai, Z., and Lee, T. (February, January 31). Autonomous Quadrotor 3D Mapping and Exploration Using Exact Occupancy Probabilities. Proceedings of the 2018 Second IEEE International Conference on Robotic Computing (IRC), Laguna Hills, CA, USA.","DOI":"10.1109\/IRC.2018.00016"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Einhorn, E., Schr\u00f6ter, C., and Gross, H. (2011, January 9\u201313). Finding the adequate resolution for grid mapping\u2014Cell sizes locally adapting on-the-fly. Proceedings of the 2011 IEEE International Conference on Robotics and Automation, Shanghai, China.","DOI":"10.1109\/ICRA.2011.5980084"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Khan, S., Wollherr, D., and Buss, M. (2015, January 26\u201330). Adaptive rectangular cuboids for 3D mapping. Proceedings of the 2015 IEEE International Conference on Robotics and Automation (ICRA), Seattle, WA, USA.","DOI":"10.1109\/ICRA.2015.7139480"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Zhu, C., Ding, R., Lin, M., and Wu, Y. (2015, January 9\u201311). A 3D Frontier-Based Exploration Tool for MAVs. Proceedings of the 2015 IEEE 27th International Conference on Tools with Artificial Intelligence (ICTAI), Vietri sul Mare, Italy.","DOI":"10.1109\/ICTAI.2015.60"},{"key":"ref_34","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_35","doi-asserted-by":"crossref","first-page":"104","DOI":"10.1016\/j.robot.2016.10.017","article-title":"Continuous mapping and localization for autonomous navigation in rough terrain using a 3D laser scanner","volume":"88","author":"Droeschel","year":"2017","journal-title":"Robot. Auton. Syst."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Cieslewski, T., Choudhary, S., and Scaramuzza, D. (2018, January 21\u201325). Data-Efficient Decentralized Visual SLAM. Proceedings of the 2018 IEEE International Conference on Robotics and Automation (ICRA), Brisbane, QLD, Australia.","DOI":"10.1109\/ICRA.2018.8461155"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"526","DOI":"10.1007\/s12559-012-9142-7","article-title":"Multi-Robot Exploration in Wireless Environments","volume":"4","author":"Pal","year":"2012","journal-title":"Cogn. Comput."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"1651","DOI":"10.1007\/s10514-018-9708-7","article-title":"Distributed inference-based multi-robot exploration","volume":"42","author":"Smith","year":"2018","journal-title":"Auton. Robot."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"856","DOI":"10.1177\/0278364920916531","article-title":"Distributed multi-robot collision avoidance via deep reinforcement learning for navigation in complex scenarios","volume":"39","author":"Fan","year":"2020","journal-title":"Int. J. Robot. Res."},{"key":"ref_40","unstructured":"Lowe, R., Wu, Y., Tamar, A., Harb, J., Abbeel, P., and Mordatch, I. (2017, January 4\u20139). Multi-agent actor-critic for mixed cooperative-competitive environments. Proceedings of the 31st International Conference on Neural Information Processing Systems, Long Beach, CA, USA."},{"key":"ref_41","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_42","doi-asserted-by":"crossref","unstructured":"Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.Y., and Berg, A.C. (2016). SSD: Single Shot MultiBox Detector. Computer Vision\u2014ECCV 2016, Springer.","DOI":"10.1007\/978-3-319-46448-0_2"},{"key":"ref_43","unstructured":"Bargoti, S., and Underwood, J. (June, January 29). Deep fruit detection in orchards. Proceedings of the IEEE International Conference on Robotics and Automation, Singapore."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Sa, I., Ge, Z., Dayoub, F., Upcroft, B., Perez, T., and McCool, C. (2016). DeepFruits: A Fruit Detection System Using Deep Neural Networks. Sensors, 16.","DOI":"10.3390\/s16081222"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"1325","DOI":"10.1109\/JPROC.2006.876927","article-title":"Distributed Multirobot Exploration and Mapping","volume":"94","author":"Fox","year":"2006","journal-title":"Proc. IEEE"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"212","DOI":"10.1016\/j.ifacol.2016.07.734","article-title":"3D Navigation Mesh Generation for Path Planning in Uneven Terrain","volume":"49","author":"Wiemann","year":"2016","journal-title":"IFAC-PapersOnLine"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Girshick, R. (2015, January 7\u201313). Fast R-CNN. Proceedings of the 2015 IEEE International Conference on Computer Vision (ICCV), Santiago, Chile.","DOI":"10.1109\/ICCV.2015.169"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/13\/4588\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T06:25:54Z","timestamp":1760163954000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/13\/4588"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,7,4]]},"references-count":47,"journal-issue":{"issue":"13","published-online":{"date-parts":[[2021,7]]}},"alternative-id":["s21134588"],"URL":"https:\/\/doi.org\/10.3390\/s21134588","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,7,4]]}}}