{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,9]],"date-time":"2026-04-09T21:33:07Z","timestamp":1775770387827,"version":"3.50.1"},"reference-count":25,"publisher":"Walter de Gruyter GmbH","issue":"1","license":[{"start":{"date-parts":[[2024,6,1]],"date-time":"2024-06-01T00:00:00Z","timestamp":1717200000000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,6,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>With the increase in the capabilities of robotic devices, there is a growing need for accurate and relevant environment maps. Current robotic devices can map their surrounding environment using a multitude of sensors as mapping sources. The challenge lies in combining these heterogeneous maps into a single, informative map to enhance the robustness of subsequent robot control algorithms. In this paper, we propose to perform map fusion as a post-processing step based on the alignment of the window of interest (WOI) from occupancy grid histograms. Initially, histograms are obtained from map pixels to determine the relevant WOI. Subsequently, they are transformed to align with a selected base image using the Manhattan distance of histogram values and the rotation angle from WOI line regression. We demonstrate that this method enables the combination of maps from multiple sources without the need for sensor calibration.<\/jats:p>","DOI":"10.2478\/acss-2024-0010","type":"journal-article","created":{"date-parts":[[2024,8,15]],"date-time":"2024-08-15T03:26:06Z","timestamp":1723692366000},"page":"78-84","source":"Crossref","is-referenced-by-count":0,"title":["Heterogeneous Map Fusion from Occupancy Grid Histograms for Mobile Robots"],"prefix":"10.2478","volume":"29","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2267-4220","authenticated-orcid":false,"given":"Aleksandrs","family":"Sisojevs","sequence":"first","affiliation":[{"name":"Riga Technical University , Riga , Latvia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Aleksandrs","family":"Korsunovs","sequence":"additional","affiliation":[{"name":"Robotic Solutions Ltd. , Riga , Latvia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Martins","family":"Banis","sequence":"additional","affiliation":[{"name":"Robotic Solutions Ltd. , Riga , Latvia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Vilnis","family":"Turkovs","sequence":"additional","affiliation":[{"name":"Robotic Solutions Ltd. , Riga , Latvia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9256-6655","authenticated-orcid":false,"given":"Reinis","family":"Cimurs","sequence":"additional","affiliation":[{"name":"Robotic Solutions Ltd. , Riga , Latvia"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"374","published-online":{"date-parts":[[2024,8,15]]},"reference":[{"key":"2026040920411997920_j_acss-2024-0010_ref_001","doi-asserted-by":"crossref","unstructured":"H. Bavle, J. L. Sanchez-Lopez, C. Cimarelli, A. Tourani, and H. Voos, \u201cFrom slam to situational awareness: Challenges and survey,\u201d Sensors, vol. 23, no.10, May 2023, Art. no. 4849. https:\/\/doi.org\/10.3390\/s23104849","DOI":"10.3390\/s23104849"},{"key":"2026040920411997920_j_acss-2024-0010_ref_002","doi-asserted-by":"crossref","unstructured":"L. P. Nalla Perumal and A. S. Arockia Doss, \u201cSensor fusion for automotive dead reckoning using GPS and IMU for accurate position and velocity estimation,\u201d Trends in Mechanical and Biomedical Design: Select Proceedings of ICMechD 2019, pp. 83\u201395, 2021. https:\/\/doi.org\/10.1007\/978-981-15-4488-0_8","DOI":"10.1007\/978-981-15-4488-0_8"},{"key":"2026040920411997920_j_acss-2024-0010_ref_003","doi-asserted-by":"crossref","unstructured":"N. El-Sheimy and Y. Li, \u201cIndoor navigation: State of the art and future trends,\u201d Satellite Navigation, vol. 2, no. 1, May 2021, Art. no. 7. https:\/\/doi.org\/10.1186\/s43020-021-00041-3","DOI":"10.1186\/s43020-021-00041-3"},{"key":"2026040920411997920_j_acss-2024-0010_ref_004","doi-asserted-by":"crossref","unstructured":"I. A. Kazerouni, L. Fitzgerald, G. Dooly, and D. Toal, \u201cA survey of state-of-the-art on visual slam,\u201d Expert Systems with Applications, vol. 205, Nov. 2022, Art. no. 117734. https:\/\/doi.org\/10.1016\/j.eswa.2022.117734","DOI":"10.1016\/j.eswa.2022.117734"},{"key":"2026040920411997920_j_acss-2024-0010_ref_005","doi-asserted-by":"crossref","unstructured":"M. Bujanca, X. Shi, M. Spear, P. Zhao, B. Lennox, and M. Luj\u00e1n, \u201cRobust slam systems: Are we there yet?\u201d in 2021 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), Prague, Czech Republic, Sep. 2021, pp. 5320\u20135327. https:\/\/doi.org\/10.1109\/IROS51168.2021.9636814","DOI":"10.1109\/IROS51168.2021.9636814"},{"key":"2026040920411997920_j_acss-2024-0010_ref_006","doi-asserted-by":"crossref","unstructured":"W. Chen, C. Zhou, G. Shang, X. Wang, Z. Li, C. Xu, and K. Hu, \u201cSlam overview: From single sensor to heterogeneous fusion,\u201d Remote Sensing, vol. 14, no. 23, Nov. 2022, Art. no. 6033. https:\/\/doi.org\/10.3390\/rs14236033","DOI":"10.3390\/rs14236033"},{"key":"2026040920411997920_j_acss-2024-0010_ref_007","unstructured":"C. V. Jones, G. E. Hall, S. J. Baron, B. Hild, S. Zickler, and J. Sinnigen, \u201cMobile cleaning robot artificial intelligence for situational awareness,\u201d U.S. Patent 10 878 294, Dec. 29, 2020."},{"key":"2026040920411997920_j_acss-2024-0010_ref_008","unstructured":"M. Munich, A. Kolling, M. Narayana, and P. Fong, \u201cMapping for autonomous mobile robots,\u201d U.S. Patent 11 249 482, Feb. 15, 2022."},{"key":"2026040920411997920_j_acss-2024-0010_ref_009","doi-asserted-by":"crossref","unstructured":"C. A. Vel\u00e1squez Hern\u00e1ndez and F. A. Prieto Ortiz, \u201cA real-time map merging strategy for robust collaborative reconstruction of unknown environments,\u201d Expert Systems with Applications, vol. 145, May 2020, Art. no. 113109. https:\/\/doi.org\/10.1016\/j.eswa.2019.113109","DOI":"10.1016\/j.eswa.2019.113109"},{"key":"2026040920411997920_j_acss-2024-0010_ref_010","doi-asserted-by":"crossref","unstructured":"Y. Xie, Y. Tang, R. Zhou, Y. Guo, and H. Shi, \u201cMap merging with terrain-adaptive density using mobile 3D laser scanner,\u201d Robotics and Autonomous Systems, vol. 134, Dec. 2020, Art. no. 103649. https:\/\/doi.org\/10.1016\/j.robot.2020.103649","DOI":"10.1016\/j.robot.2020.103649"},{"key":"2026040920411997920_j_acss-2024-0010_ref_011","doi-asserted-by":"crossref","unstructured":"Z. Li, B. Xu, D. Wu, K. Zhao, S. Chen, M. Lu, and J. Cong, \u201cA YOLOGGCNN based grasping framework for mobile robots in unknown environments,\u201d Expert Systems with Applications, vol. 225, Sep. 2023, Art. no. 119993. https:\/\/doi.org\/10.1016\/j.eswa.2023.119993","DOI":"10.1016\/j.eswa.2023.119993"},{"key":"2026040920411997920_j_acss-2024-0010_ref_012","doi-asserted-by":"crossref","unstructured":"N. Banerjee et al., \u201cLifelong mapping in the wild: Novel strategies for ensuring map stability and accuracy over time evaluated on thousands of robots,\u201d Robotics and Autonomous Systems, vol. 164, Jun. 2023, Art. no. 104403. https:\/\/doi.org\/10.1016\/j.robot.2023.104403","DOI":"10.1016\/j.robot.2023.104403"},{"key":"2026040920411997920_j_acss-2024-0010_ref_013","doi-asserted-by":"crossref","unstructured":"S.-H. Chan, P.-T. Wu, and L.-C. Fu, \u201cRobust 2D indoor localization through laser slam and visual slam fusion,\u201d in 2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC), Miyazaki, Japan, Oct. 2018, pp. 1263\u20131268. https:\/\/doi.org\/10.1109\/SMC.2018.00221","DOI":"10.1109\/SMC.2018.00221"},{"key":"2026040920411997920_j_acss-2024-0010_ref_014","doi-asserted-by":"crossref","unstructured":"Y. Xu, Y. Ou, and T. Xu, \u201cSlam of robot based on the fusion of vision and lidar,\u201d in 2018 IEEE International Conference on Cyborg and Bionic Systems (CBS), Shenzhen, China, Oct. 2018, pp. 121\u2013126. https:\/\/doi.org\/10.1109\/CBS.2018.8612212","DOI":"10.1109\/CBS.2018.8612212"},{"key":"2026040920411997920_j_acss-2024-0010_ref_015","doi-asserted-by":"crossref","unstructured":"X. Xu, L. Zhang, J. Yang, C. Cao, W. Wang, Y. Ran, Z. Tan, and M. Luo, \u201cA review of multi-sensor fusion slam systems based on 3D lidar,\u201d Remote Sensing, vol. 14, no. 12, Jun. 2022, Art. no. 2835. https:\/\/doi.org\/10.3390\/rs14122835","DOI":"10.3390\/rs14122835"},{"key":"2026040920411997920_j_acss-2024-0010_ref_016","doi-asserted-by":"crossref","unstructured":"M. F. Ahmed, K. Masood, V. Fremont, and I. Fantoni, \u201cActive slam: A review on last decade,\u201d Sensors, vol. 23, no. 19, Sep. 2023, Art. no. 8097. https:\/\/doi.org\/10.3390\/s23198097","DOI":"10.3390\/s23198097"},{"key":"2026040920411997920_j_acss-2024-0010_ref_017","doi-asserted-by":"crossref","unstructured":"H. Zhou, Z. Yao, and M. Lu, \u201cLidar\/UWB fusion based SLAM with anti-degeneration capability,\u201d IEEE Transactions on Vehicular Technology, vol. 70, no. 1, pp. 820\u2013830, Dec. 2020. https:\/\/doi.org\/10.1109\/TVT.2020.3045767","DOI":"10.1109\/TVT.2020.3045767"},{"key":"2026040920411997920_j_acss-2024-0010_ref_018","doi-asserted-by":"crossref","unstructured":"X. Dang, Z. Rong, and X. Liang, \u201cSensor fusion-based approach to eliminating moving objects for SLAM in dynamic environments,\u201d Sensors, vol. 21, no. 1, Jan. 2021, Art. no. 230. https:\/\/doi.org\/10.3390\/s21010230","DOI":"10.3390\/s21010230"},{"key":"2026040920411997920_j_acss-2024-0010_ref_019","doi-asserted-by":"crossref","unstructured":"I. Andersone, \u201cHeterogeneous map merging: State of the art,\u201d Robotics, vol. 8, no. 3, Aug. 2019, Art. no. 74. https:\/\/doi.org\/10.3390\/robotics8030074","DOI":"10.3390\/robotics8030074"},{"key":"2026040920411997920_j_acss-2024-0010_ref_020","doi-asserted-by":"crossref","unstructured":"C. Debeunne and D. Vivet, \u201cA review of visual-lidar fusion based simultaneous localization and mapping,\u201d Sensors, vol. 20, no. 7, Apr. 2020, Art. no. 2068. https:\/\/doi.org\/10.3390\/s20072068","DOI":"10.3390\/s20072068"},{"key":"2026040920411997920_j_acss-2024-0010_ref_021","doi-asserted-by":"crossref","unstructured":"F. Chanier, P. Checchin, C. Blanc, and L. Trassoudaine, \u201cMap fusion based on a multi-map SLAM framework,\u201d in 2008 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems, Seoul, Korea (South), Aug. 2008, pp. 533\u2013538. https:\/\/doi.org\/10.1109\/MFI.2008.4648050","DOI":"10.1109\/MFI.2008.4648050"},{"key":"2026040920411997920_j_acss-2024-0010_ref_022","doi-asserted-by":"crossref","unstructured":"Y. Lu, J. Lee, S.-H. Yeh, H.-M. Cheng, B. Chen, and D. Song, \u201cSharing heterogeneous spatial knowledge: Map fusion between asynchronous monocular vision and lidar or other prior inputs,\u201d in Robotics Research: The 18th International Symposium ISRR, Nov. 2020, pp. 727\u2013741. https:\/\/doi.org\/10.1007\/978-3-030-28619-4_51","DOI":"10.1007\/978-3-030-28619-4_51"},{"key":"2026040920411997920_j_acss-2024-0010_ref_023","doi-asserted-by":"crossref","unstructured":"Y. Megahed, A. Shaker, and W. Y. Yan, \u201cFusion of airborne lidar point clouds and aerial images for heterogeneous land-use urban mapping,\u201d Remote Sensing, vol. 13, no. 4, Feb. 2021, Art. no. 814. https:\/\/doi.org\/10.3390\/rs13040814","DOI":"10.3390\/rs13040814"},{"key":"2026040920411997920_j_acss-2024-0010_ref_024","doi-asserted-by":"crossref","unstructured":"B. Zhang, J. Liu, and H. Chen, \u201cAMCL based map fusion for multi-robot SLAM with heterogenous sensors,\u201d in 2013 IEEE International Conference on Information and Automation (ICIA), Yinchuan, China, Aug. 2013, pp. 822\u2013827. https:\/\/doi.org\/10.1109\/ICInfA.2013.6720407","DOI":"10.1109\/ICInfA.2013.6720407"},{"key":"2026040920411997920_j_acss-2024-0010_ref_025","doi-asserted-by":"crossref","unstructured":"Z.-g. Liu, L. Zhang, G. Li, and Y. He, \u201cChange detection in heterogeneous remote sensing images based on the fusion of pixel transformation,\u201d in 2017 20th International Conference on Information Fusion (Fusion), Xi'an, China, Jul. 2017, pp. 1\u20136. https:\/\/doi.org\/10.23919\/ICIF.2017.8009656","DOI":"10.23919\/ICIF.2017.8009656"}],"container-title":["Applied Computer Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/reference-global.com\/pdf\/10.2478\/acss-2024-0010","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,9]],"date-time":"2026-04-09T20:41:34Z","timestamp":1775767294000},"score":1,"resource":{"primary":{"URL":"https:\/\/reference-global.com\/article\/10.2478\/acss-2024-0010"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,6,1]]},"references-count":25,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2024,8,15]]},"published-print":{"date-parts":[[2024,6,1]]}},"alternative-id":["10.2478\/acss-2024-0010"],"URL":"https:\/\/doi.org\/10.2478\/acss-2024-0010","relation":{},"ISSN":["2255-8691"],"issn-type":[{"value":"2255-8691","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,6,1]]}}}