<?xml version="1.0" encoding="UTF-8"?>
<!DOCTYPE article PUBLIC "-//NLM//DTD JATS (Z39.96) Journal Publishing DTD v1.4 20241031//EN"
        "https://jats.nlm.nih.gov/publishing/1.4/JATS-journalpublishing1-4.dtd">
<article  article-type="research-article"        dtd-version="1.4">
            <front>

                <journal-meta>
                                                                <journal-id>ijeg</journal-id>
            <journal-title-group>
                                                                                    <journal-title>International Journal of Engineering and Geosciences</journal-title>
            </journal-title-group>
                                        <issn pub-type="epub">2548-0960</issn>
                                                                                            <publisher>
                    <publisher-name>Murat YAKAR</publisher-name>
                </publisher>
                    </journal-meta>
                <article-meta>
                                        <article-id pub-id-type="doi">10.26833/ijeg.1519533</article-id>
                                                                <article-categories>
                                            <subj-group  xml:lang="en">
                                                            <subject>Geomatic Engineering (Other)</subject>
                                                    </subj-group>
                                            <subj-group  xml:lang="tr">
                                                            <subject>Jeomatik Mühendisliği (Diğer)</subject>
                                                    </subj-group>
                                    </article-categories>
                                                                                                                                                        <title-group>
                                                                                                                                                            <article-title>Accuracy Evaluation of LiDAR-SLAM Based 2-Dimensional Modelling for Indoor Environment: A Case Study</article-title>
                                                                                                    </title-group>
            
                                                    <contrib-group content-type="authors">
                                                                        <contrib contrib-type="author">
                                                                    <contrib-id contrib-id-type="orcid">
                                        https://orcid.org/0009-0006-3344-9236</contrib-id>
                                                                <name>
                                    <surname>Başaran</surname>
                                    <given-names>Aleyna</given-names>
                                </name>
                                                                    <aff>ONDOKUZ MAYIS UNIVERSITY</aff>
                                                            </contrib>
                                                    <contrib contrib-type="author">
                                                                    <contrib-id contrib-id-type="orcid">
                                        https://orcid.org/0000-0002-9485-874X</contrib-id>
                                                                <name>
                                    <surname>İlçi</surname>
                                    <given-names>Veli</given-names>
                                </name>
                                                                    <aff>ONDOKUZ MAYIS ÜNİVERSİTESİ</aff>
                                                            </contrib>
                                                                                </contrib-group>
                        
                                        <pub-date pub-type="pub" iso-8601-date="20250201">
                    <day>02</day>
                    <month>01</month>
                    <year>2025</year>
                </pub-date>
                                        <volume>10</volume>
                                        <issue>1</issue>
                                        <fpage>74</fpage>
                                        <lpage>83</lpage>
                        
                        <history>
                                    <date date-type="received" iso-8601-date="20240720">
                        <day>07</day>
                        <month>20</month>
                        <year>2024</year>
                    </date>
                                                    <date date-type="accepted" iso-8601-date="20240917">
                        <day>09</day>
                        <month>17</month>
                        <year>2024</year>
                    </date>
                            </history>
                                        <permissions>
                    <copyright-statement>Copyright © 2016, International Journal of Engineering and Geosciences</copyright-statement>
                    <copyright-year>2016</copyright-year>
                    <copyright-holder>International Journal of Engineering and Geosciences</copyright-holder>
                </permissions>
            
                                                                                                                        <abstract><p>The rapid development of sensor technologies has led to smaller sensor sizes and lower costs. Today, the easy-of-use purchasing of sensors such as cameras, Light Detection and Ranging (LiDAR), Radio Detection and Ranging (RADAR), Inertial Measurement Units (IMUs), and Global Navigation Satellite System (GNSS) receivers have led to significant developments in many applications such as robotics and unmanned vehicles. Sensor data is transformed into information or products thanks to the methods. Simultaneous Localization and Mapping (SLAM) is one of the critical methods in which the vehicle&#039;s location is determined, and the environment is modelled. This method can realize applications using detection sensors such as cameras, LiDAR, or RADAR. This study aimed to model an indoor area with a two-dimensional (2D) LiDAR sensor placed on an Unmanned Ground Vehicle (UGV) and to analyse the accuracy of the produced model. Normal Distribution Transform (NDT) - Particle Swarm Optimization (PSO) algorithm was used to generate the 2D model from the collected LiDAR data. The NDT-PSO algorithm was executed on the Robot Operating System (ROS) installed on the Jetson Nano Developer Kit, and a real-time 2D model of the working area was processed. The reference lengths of the 75 facades in the 232 m2 indoor space were measured using a total station and calculated with CAD software. Percent error values were evaluated by comparing the reference and model lengths of the facades</p></abstract>
                                                            
            
                                                                                        <kwd-group>
                                                    <kwd>LiDAR</kwd>
                                                    <kwd>  SLAM</kwd>
                                                    <kwd>  Modelling</kwd>
                                                    <kwd>  NDT</kwd>
                                            </kwd-group>
                            
                                                                                                                                                <funding-group specific-use="FundRef">
                    <award-group>
                                                    <funding-source>
                                <named-content content-type="funder_name">Ondokuz Mayıs University Scientific Research Projects</named-content>
                            </funding-source>
                                                                            <award-id>PYO.MUH.1906.22.002</award-id>
                                            </award-group>
                </funding-group>
                                </article-meta>
    </front>
    <back>
                            <ref-list>
                                    <ref id="ref1">
                        <label>1</label>
                        <mixed-citation publication-type="journal">Han, S., &amp; Xi, Z. (2020). Dynamic Scene Semantics SLAM Based on Semantic Segmentation. IEEE Access, 8, 43563–43570. https://doi.org/10.1109/ACCESS.2020.2977684</mixed-citation>
                    </ref>
                                    <ref id="ref2">
                        <label>2</label>
                        <mixed-citation publication-type="journal">Tee, Y. K., &amp; Han, Y. C. (2021). Lidar-Based 2D SLAM for Mobile Robot in an Indoor Environment: A Review. In 2021 International Conference on Green Energy, Computing and Sustainable Technology, (GECOST) (pp. 1–7). Miri: IEEE. https://doi.org/10.1109/GECOST52368.2021.9538731</mixed-citation>
                    </ref>
                                    <ref id="ref3">
                        <label>3</label>
                        <mixed-citation publication-type="journal">Han, X., Li, S., Wang, X., &amp; Zhou, W. (2021). Semantic Mapping for Mobile Robots in Indoor Scenes: A Survey. Information, 12(2), 1–14. https://doi.org/10.3390/info12020092</mixed-citation>
                    </ref>
                                    <ref id="ref4">
                        <label>4</label>
                        <mixed-citation publication-type="journal">Özbayrak, S., &amp; İlçi, V. (2024). Visual-SLAM based 3-dimensional modelling of indoor environments. International Journal of Engineering and Geosciences, 9(3), 368–376. https://doi.org/10.26833/ijeg.1459216</mixed-citation>
                    </ref>
                                    <ref id="ref5">
                        <label>5</label>
                        <mixed-citation publication-type="journal">Ghorpade, D., Thakare, A. D., &amp; Doiphode, S. (2017). Obstacle Detection and Avoidance Algorithm for Autonomous Mobile Robot using 2D LiDAR. In 2017 Third International Conference on Computing, Communication, Control and Automation (ICCUBEA) (pp. 1–6). Pune, India: IEEE. https://doi.org/10.1109/ICCUBEA.2017.8463846</mixed-citation>
                    </ref>
                                    <ref id="ref6">
                        <label>6</label>
                        <mixed-citation publication-type="journal">Gomes, D., Alvarez, M., Brancalião, L., Carneiro, J., Gonçalves, G., Costa, P., … Pinto, V. H. (2022). Data Analysis for Trajectory Generation for a Robot Manipulator Using Data from a 2D Industrial Laser. Machines, 10(10), 907. https://doi.org/10.3390/machines10100907</mixed-citation>
                    </ref>
                                    <ref id="ref7">
                        <label>7</label>
                        <mixed-citation publication-type="journal">Shang, Y., Wang, H., Qin, W., Wang, Q., Liu, H., Yin, Y., … Meng, Z. (2023). Design and Test of Obstacle Detection and Harvester Pre-Collision System Based on 2D Lidar. Agronomy, 13(2), 388. https://doi.org/10.3390/agronomy13020388</mixed-citation>
                    </ref>
                                    <ref id="ref8">
                        <label>8</label>
                        <mixed-citation publication-type="journal">Afrisal, H., Nugraha, G. K., Nanda, A. A., Setiyadi, A. D., Toirov, O., Ismail, R., … Setiawan, I. (2022). Mobile Robotic-Arm Development for A Small-Scale Inter-Room Logistic Delivery using 2D LIDAR-guided Navigation. Teknik, 43(2), 158–167. https://doi.org/10.14710/teknik.v43i2.45642</mixed-citation>
                    </ref>
                                    <ref id="ref9">
                        <label>9</label>
                        <mixed-citation publication-type="journal">Sui, L., &amp; Lin, L. (2020). Design of Household Cleaning Robot Based on Low-cost 2D LIDAR SLAM. In 2020 International Symposium on Autonomous Systems, (ISAS) (pp. 223–227). IEEE. https://doi.org/10.1109/ISAS49493.2020.9378863</mixed-citation>
                    </ref>
                                    <ref id="ref10">
                        <label>10</label>
                        <mixed-citation publication-type="journal">Kaderli, L. (2021). Documentation Methods from Tradition to the Present: Case Study Cappadocia. Advanced LiDAR, 1(1), 18–26.</mixed-citation>
                    </ref>
                                    <ref id="ref11">
                        <label>11</label>
                        <mixed-citation publication-type="journal">Niloy, M. A. K., Shama, A., Chakrabortty, R. K., Ryan, M. J., Badal, F. R., Tasneem, Z., … Saha, D. K. (2021). Critical Design and Control Issues of Indoor Autonomous Mobile Robots: A Review. IEEE Access, 9, 35338–35370. https://doi.org/10.1109/ACCESS.2021.3062557</mixed-citation>
                    </ref>
                                    <ref id="ref12">
                        <label>12</label>
                        <mixed-citation publication-type="journal">Koca, B., &amp; Ceylan, A. (2018). Uydu Konum Belirleme Sistemlerindeki (GNSS) Güncel Durum ve Son Gelişmeler. Geomatik Dergisi, 3(1), 63–73.</mixed-citation>
                    </ref>
                                    <ref id="ref13">
                        <label>13</label>
                        <mixed-citation publication-type="journal">İlçi, V., &amp; Peker, A. U. (2022). The Kinematic Performance of Real-Time PPP Services in Challenging Environment. Measurement, 189, 110434. https://doi.org/10.1016/j.measurement.2021.110434</mixed-citation>
                    </ref>
                                    <ref id="ref14">
                        <label>14</label>
                        <mixed-citation publication-type="journal">Li, Y., &amp; Ibanez-Guzman, J. (2020). Lidar for Autonomous Driving: The Principles, Challenges, and Trends for Automotive Lidar and Perception Systems. IEEE Signal Processing Magazine, 37(4), 50–61. https://doi.org/10.1109/MSP.2020.2973615</mixed-citation>
                    </ref>
                                    <ref id="ref15">
                        <label>15</label>
                        <mixed-citation publication-type="journal">İlçi, V., Gülal, E., &amp; Alkan, R. M. (2018). An Investigation of Different Wi-Fi Signal Behaviours and Their Effects on Indoor Positioning Accuracy. Survey Review, 50(362), 404–411. https://doi.org/10.1080/00396265.2017.1292672</mixed-citation>
                    </ref>
                                    <ref id="ref16">
                        <label>16</label>
                        <mixed-citation publication-type="journal">İlçi, V., Gülal, E., &amp; Alkan, R. M. (2020). Performance Comparison of 2.4 and 5 GHz WiFi Signals and Proposing a New Method for Mobile Indoor Positioning. Wireless Personal Communications, 110, 1493–1511. https://doi.org/10.1007/s11277-019-06797-x</mixed-citation>
                    </ref>
                                    <ref id="ref17">
                        <label>17</label>
                        <mixed-citation publication-type="journal">Onyekpe, U., Palade, V., &amp; Kanarachos, S. (2021). Learning to Localise Automated Vehicles in Challenging Environments Using Inertial Navigation Systems (INS). Applied Sciences, 11(3), 1270. https://doi.org/10.3390/app11031270</mixed-citation>
                    </ref>
                                    <ref id="ref18">
                        <label>18</label>
                        <mixed-citation publication-type="journal">Gürtürk, M., &amp; İlçi, V. (2022). The Performance Evaluation of PPK and PPP-based Loosely Coupled Integration in Wooded and Urban Areas. Earth Sciences Research Journal, 26, 211–220. https://doi.org/10.15446/esrj.v26n3.100518</mixed-citation>
                    </ref>
                                    <ref id="ref19">
                        <label>19</label>
                        <mixed-citation publication-type="journal">Jiang, W., Li, Y., Rizos, C., Cai, B., &amp; Shangguan, W. (2017). Seamless Indoor-Outdoor Navigation based on GNSS, INS and Terrestrial Ranging Techniques. Journal of Navigation, 70, 1183–1204. https://doi.org/10.1017/S037346331700042X</mixed-citation>
                    </ref>
                                    <ref id="ref20">
                        <label>20</label>
                        <mixed-citation publication-type="journal">Dai, Y., Wu, J., Wang, D., &amp; Watanabe, K. (2023). A Review of Common Techniques for Visual Simultaneous Localization and Mapping. Journal of Robotics, 2023, 1–21. https://doi.org/10.1155/2023/8872822</mixed-citation>
                    </ref>
                                    <ref id="ref21">
                        <label>21</label>
                        <mixed-citation publication-type="journal">Chen, Y., Zhou, Y., Lv, Q., &amp; Deveerasetty, K. K. (2018). A Review of V-SLAM. In International Conference on Information and Automation (pp. 603–608). Wuyi Mountain, China: IEEE.</mixed-citation>
                    </ref>
                                    <ref id="ref22">
                        <label>22</label>
                        <mixed-citation publication-type="journal">Huang, L. (2021). Review on LiDAR-based SLAM Techniques. In 2021 International Conference on Signal Processing and Machine Learning, (CONF-SPML) (pp. 163–168). Stanford, CA, USA: IEEE. https://doi.org/10.1109/CONF-SPML54095.2021.00040</mixed-citation>
                    </ref>
                                    <ref id="ref23">
                        <label>23</label>
                        <mixed-citation publication-type="journal">Sarıtaş, B., &amp; Kaplan, G. (2024). A Comprehensive Study on Enhanced Accuracy Analysis of LIDAR Data: The Example of Skopje. Advanced LiDAR, 4(1), 9–18.</mixed-citation>
                    </ref>
                                    <ref id="ref24">
                        <label>24</label>
                        <mixed-citation publication-type="journal">Sevgen, S. C. (2019). Airborne LiDAR Data Classification in Complex Urban Area Using Random Forest: A Case Study of Bergama, Turkey. International Journal of Engineering and Geosciences, 4(1), 45–51. https://doi.org/10.26833/ijeg.440828</mixed-citation>
                    </ref>
                                    <ref id="ref25">
                        <label>25</label>
                        <mixed-citation publication-type="journal">Demirel, Y., &amp; Türk, T. (2024, December 31). Assessment of the location accuracy of points obtained with a low-cost Lidar scanning system and GNSS method. Mersin Photogrammetry Journal. Mersin University. https://doi.org/10.53093/mephoj.1540159</mixed-citation>
                    </ref>
                                    <ref id="ref26">
                        <label>26</label>
                        <mixed-citation publication-type="journal">He, F., &amp; Zhang, L. (2023). Design of Indoor Security Robot based on Robot Operating System. Journal of Computer and Communications, 11(5), 93–107. https://doi.org/10.4236/jcc.2023.115008</mixed-citation>
                    </ref>
                                    <ref id="ref27">
                        <label>27</label>
                        <mixed-citation publication-type="journal">Olalekan, A. F., Sagor, J. A., Hasan, M. H., &amp; Oluwatobi, A. S. (2021). Comparison of Two SLAM Algorithms Provided by ROS (Robot Operating System). In 2021 2nd International Conference for Emerging Technology (INCET) (pp. 1–5). Belagavi, India: IEEE. https://doi.org/10.1109/INCET51464.2021.9456164</mixed-citation>
                    </ref>
                                    <ref id="ref28">
                        <label>28</label>
                        <mixed-citation publication-type="journal">Nagla, S. (2020). 2D Hector SLAM of Indoor Mobile Robot using 2D Lidar. In 2020 2nd International Conference on Power, Energy, Control and Transmission Systems (ICPECTS) (pp. 1–4). Chennai, India: IEEE. https://doi.org/10.1109/ICPECTS49113.2020.9336995</mixed-citation>
                    </ref>
                                    <ref id="ref29">
                        <label>29</label>
                        <mixed-citation publication-type="journal">Roriz, R., Cabral, J., &amp; Gomes, T. (2022). Automotive LiDAR Technology: A Survey. IEEE Transactions on Intelligent Transportation Systems, 23(7), 6282–6297. https://doi.org/10.1109/TITS.2021.3086804</mixed-citation>
                    </ref>
                                    <ref id="ref30">
                        <label>30</label>
                        <mixed-citation publication-type="journal">Protopopov, V. V. (2009). Laser Heterodyne Radars and Lidars (Vol. 149). Berlin, Heidelberg: Springer Series in Optical Sciences.</mixed-citation>
                    </ref>
                                    <ref id="ref31">
                        <label>31</label>
                        <mixed-citation publication-type="journal">Gao, B., Hu, G., Zhong, Y., &amp; Zhu, X. (2021). Cubature Kalman Filter with Both Adaptability and Robustness for Tightly-Coupled GNSS/INS Integration. IEEE Sensors Journal, 21(13), 14997–15011. https://doi.org/10.1109/JSEN.2021.3073963</mixed-citation>
                    </ref>
                                    <ref id="ref32">
                        <label>32</label>
                        <mixed-citation publication-type="journal">Bitenc, M., Lindenbergh, R., Khoshelham, K., &amp; van Waarden, A. P. (2011). Evaluation of a LIDAR Land-Based Mobile Mapping System for Monitoring Sandy Coasts. Remote Sensing, 3(7), 1472–1491. https://doi.org/10.3390/rs3071472</mixed-citation>
                    </ref>
                                    <ref id="ref33">
                        <label>33</label>
                        <mixed-citation publication-type="journal">Uthe, E. E. (1983). Application of Surface Based and Airborne Lidar Systems for Environmental Monitoring. Journal of the Air Pollution Control Association, 33(12), 1149–1155. https://doi.org/10.1080/00022470.1983.10465705</mixed-citation>
                    </ref>
                                    <ref id="ref34">
                        <label>34</label>
                        <mixed-citation publication-type="journal">Mehendale, N. , &amp; Neoge, S. ,. (2020). Review on LiDAR technology. SSRN, 1–9. https://doi.org/http://dx.doi.org/10.2139/ssrn.3604309</mixed-citation>
                    </ref>
                                    <ref id="ref35">
                        <label>35</label>
                        <mixed-citation publication-type="journal">Ozdemir, S., Akbulut, Z., Karsli, F., &amp; Acar, H. (2021). Automatic extraction of trees by using multiple return properties of the lidar point cloud. International Journal of Engineering and Geosciences, 6(1), 20–26. https://doi.org/10.26833/ijeg.668352</mixed-citation>
                    </ref>
                                    <ref id="ref36">
                        <label>36</label>
                        <mixed-citation publication-type="journal">Peng, Y. , Qu, D. , Zhong, Y. , Xie, S. , Luo, J. , &amp; Gu, J. (2015). The Obstacle Detection and Obstacle Avoidance Algorithm Based on 2-D Lidar. In 2015 IEEE International Conference on Information and Automation (pp. 1648–1653). Lijiang, China: IEEE.</mixed-citation>
                    </ref>
                                    <ref id="ref37">
                        <label>37</label>
                        <mixed-citation publication-type="journal">Promneewat, K., &amp; Taksavasu, T. (2024). Performance of Affordable 2D Cave Scanning Technique from LiDAR for Constructing 3D Cave Models. Advanced LiDAR, 4(1), 1–8.</mixed-citation>
                    </ref>
                                    <ref id="ref38">
                        <label>38</label>
                        <mixed-citation publication-type="journal">Meng, J., Wan, L., Wang, S., Jiang, L., Li, G., Wu, L., &amp; Xie, Y. (2021). Efficient and Reliable LiDAR-Based Global Localization of Mobile Robots Using Multiscale/Resolution Maps. IEEE Transactions on Instrumentation and Measurement, 70, 1–15. https://doi.org/10.1109/TIM.2021.3093933</mixed-citation>
                    </ref>
                                    <ref id="ref39">
                        <label>39</label>
                        <mixed-citation publication-type="journal">Queralta, J. P., Yuhong, F., Salomaa, L., Qingqing, L., Gia, T. N., Zou, Z., &amp; Westerlund, T. (2019). FPGA-based Architecture for a Low-Cost 3D Lidar Design and Implementation from Multiple Rotating 2D Lidars with ROS. In 2019 IEEE Sensors (pp. 1–4). Montreal, QC: IEEE. https://doi.org/10.1109/SENSORS43011.2019.8956928</mixed-citation>
                    </ref>
                                    <ref id="ref40">
                        <label>40</label>
                        <mixed-citation publication-type="journal">Nguyen, X. T., Kim, H., &amp; Lee, H. J. (2020). A Gradient-Aware Line Sampling Algorithm for LiDAR Scanners. IEEE Sensors Journal, 20(16), 9283–9292. https://doi.org/10.1109/JSEN.2020.2986819</mixed-citation>
                    </ref>
                                    <ref id="ref41">
                        <label>41</label>
                        <mixed-citation publication-type="journal">Raj, T., Hashim, F. H., Huddin, A. B., Ibrahim, M. F., &amp; Hussain, A. (2020). A survey on LiDAR scanning Mechanisms. Electronics, 9(5), 741. https://doi.org/10.3390/electronics9050741</mixed-citation>
                    </ref>
                                    <ref id="ref42">
                        <label>42</label>
                        <mixed-citation publication-type="journal">Behroozpour, B., Sandborn, P. A. M., Wu, M. C., &amp; Boser, B. E. (2017). Lidar System Architectures and Circuits. IEEE Communications Magazine, 55(10), 135–142. https://doi.org/10.1109/MCOM.2017.1700030</mixed-citation>
                    </ref>
                                    <ref id="ref43">
                        <label>43</label>
                        <mixed-citation publication-type="journal">Hassan, T., Fath-Allah, T., Elhabiby, M., Awad, A., &amp; El-Tokhey, M. (2022). Detection of GNSS no-line of Sight Signals Using LiDAR Sensors for Intelligent Transportation Systems. Survey Review, 54(385), 301–309. https://doi.org/10.1080/00396265.2021.1937458</mixed-citation>
                    </ref>
                                    <ref id="ref44">
                        <label>44</label>
                        <mixed-citation publication-type="journal">Günen, M. A., Erkan, İ., Aliyazıcıoğlu, Ş., &amp; Kumaş, C. (2023). Investigation of geometric object and indoor mapping capacity of Apple iPhone 12 Pro LiDAR. Mersin Photogrammetry Journal, 5(2), 82–89. https://doi.org/10.53093/mephoj.1354998</mixed-citation>
                    </ref>
                                    <ref id="ref45">
                        <label>45</label>
                        <mixed-citation publication-type="journal">Dai, Z., Wolf, A., Ley, P. P., Glück, T., Sundermeier, M. C., &amp; Lachmayer, R. (2022). Requirements for Automotive LiDAR Systems. Sensors, 22(19), 7532. https://doi.org/10.3390/s22197532</mixed-citation>
                    </ref>
                                    <ref id="ref46">
                        <label>46</label>
                        <mixed-citation publication-type="journal">Z. J. Chong, B. Qin, T. Bandyopadhyay, M. H. Ang, E. Frazzoli, &amp; D. Rus. (2013). Mapping with Synthetic 2D LIDAR in 3D Urban Environment. In 2013 IEEE/RSJ International Conference on Intelligent Robots and Syste (IROS) (pp. 4715–4720). Tokyo: IEEE.</mixed-citation>
                    </ref>
                                    <ref id="ref47">
                        <label>47</label>
                        <mixed-citation publication-type="journal">Gul, F., Rahiman, W., &amp; Nazli Alhady, S. S. (2019). A Comprehensive Study for Robot Navigation Techniques. Cogent Engineering, 6(1). https://doi.org/10.1080/23311916.2019.1632046</mixed-citation>
                    </ref>
                                    <ref id="ref48">
                        <label>48</label>
                        <mixed-citation publication-type="journal">Habib, M. K. (2007). Real Time Mapping and Dynamic Navigation for Mobile Robots. International Journal of Advanced Robotic Systems, 4(3), 323–338.</mixed-citation>
                    </ref>
                                    <ref id="ref49">
                        <label>49</label>
                        <mixed-citation publication-type="journal">Khan, M. U., Zaidi, S. A. A., Ishtiaq, A., Bukhari, S. U. R., Samer, S., &amp; Farman, A. (2021). A Comparative Survey of LiDAR-SLAM and LiDAR based Sensor Technologies. In 2021 Mohammad Ali Jinnah University International Conference on Computing (MAJICC) (pp. 1–8). Karachi, Pakistan: IEEE. https://doi.org/10.1109/MAJICC53071.2021.9526266</mixed-citation>
                    </ref>
                                    <ref id="ref50">
                        <label>50</label>
                        <mixed-citation publication-type="journal">Rivai, M., Hutabarat, D., &amp; Jauhar Nafis, Z. M. (2020). 2D mapping using omni-directional mobile robot equipped with LiDAR. Telkomnika (Telecommunication Computing Electronics and Control), 18(3), 1467–1474. https://doi.org/10.12928/TELKOMNIKA.v18i3.14872</mixed-citation>
                    </ref>
                                    <ref id="ref51">
                        <label>51</label>
                        <mixed-citation publication-type="journal">Zheng, T., Duan, Z., Wang, J., Lu, G., Li, S., &amp; Yu, Z. (2021). Research on Distance Transform and Neural Network Lidar Information Sampling Classification-Based Semantic Segmentation of 2D Indoor Room Maps. Sensors, 21(4), 1–20. https://doi.org/10.3390/s21041365</mixed-citation>
                    </ref>
                                    <ref id="ref52">
                        <label>52</label>
                        <mixed-citation publication-type="journal">Petrlik, M., Krajnik, T., &amp; Saska, M. (2021). LIDAR-based Stabilization, Navigation and Localization for UAVs Operating in Dark Indoor Environments. In 2021 International Conference on Unmanned Aircraft Systems (ICUAS) (pp. 243–251). Athens, Greece: ICUAS. https://doi.org/10.1109/ICUAS51884.2021.9476837</mixed-citation>
                    </ref>
                                    <ref id="ref53">
                        <label>53</label>
                        <mixed-citation publication-type="journal">Bae, S. H., Joo, S. H., Choi, J. H., Park, H. J., &amp; Kuc, T. Y. (2022). Localization System Through 2D LiDAR based Semantic Feature For Indoor Robot. In 2022 19th International Conference on Ubiquitous Robots (UR) (pp. 338–342). Jeju, Korea: IEEE. https://doi.org/10.1109/UR55393.2022.9826250</mixed-citation>
                    </ref>
                                    <ref id="ref54">
                        <label>54</label>
                        <mixed-citation publication-type="journal">P, T. S. , P, S. , Muppidi, A. J. , &amp; Pagala, P. S. (2020). Analysis of Computational Need of 2D-SLAM Algorithms for Unmanned Ground Vehicle. In Proceedings of the International Conference on Intelligent Computing and Control Systems (pp. 230–235). Madurai: IEEE.</mixed-citation>
                    </ref>
                                    <ref id="ref55">
                        <label>55</label>
                        <mixed-citation publication-type="journal">Eliwa, M., Adham, A., Sami, I., &amp; Eldeeb, M. (2017). A critical comparison Between Fast and Hector SLAM Algorithms. REST Journal on Emerging trends in Modelling and Manufacturing, 3(2), 44–49.</mixed-citation>
                    </ref>
                                    <ref id="ref56">
                        <label>56</label>
                        <mixed-citation publication-type="journal">Trejos, K., Rincón, L., Bolaños, M., Fallas, J., &amp; Marín, L. (2022). 2D SLAM Algorithms Characterization, Calibration, and Comparison Considering Pose Error, Map Accuracy as Well as CPU and Memory Usage. Sensors, 22(18), 6903. https://doi.org/10.3390/s22186903</mixed-citation>
                    </ref>
                                    <ref id="ref57">
                        <label>57</label>
                        <mixed-citation publication-type="journal">Das, A., &amp; Waslander, S. L. (2014). Scan Registration Using Segmented Region Growing NDT. International Journal of Robotics Research, 33(13), 1645–1663. https://doi.org/10.1177/0278364914539404</mixed-citation>
                    </ref>
                                    <ref id="ref58">
                        <label>58</label>
                        <mixed-citation publication-type="journal">Prados Sesmero, C., Villanueva Lorente, S., &amp; Di Castro, M. (2021). Graph SLAM Built Over Point Clouds Matching for Robot Localization in Tunnels. Sensors, 21(16), 5340. https://doi.org/10.3390/s21165340</mixed-citation>
                    </ref>
                                    <ref id="ref59">
                        <label>59</label>
                        <mixed-citation publication-type="journal">Jang, K. W., Jeong, W. J., &amp; Kang, Y. (2022). Development of a GPU-Accelerated NDT Localization Algorithm for GNSS-Denied Urban Areas. Sensors, 22(5), 1913. https://doi.org/10.3390/s22051913</mixed-citation>
                    </ref>
                                    <ref id="ref60">
                        <label>60</label>
                        <mixed-citation publication-type="journal">Lee, J., Lee, K., Yoo, A., &amp; Moon, C. (2020). Design and Implementation of Edge-Fog-Cloud System Through HD Map Generation from Lidar Data of Autonomous Vehicles. Electronics, 9(12), 1–15. https://doi.org/10.3390/electronics9122084</mixed-citation>
                    </ref>
                                    <ref id="ref61">
                        <label>61</label>
                        <mixed-citation publication-type="journal">Kan, Y. C., Hsu, L. T., &amp; Chung, E. (2021). Performance Evaluation on Map-Based NDT Scan Matching Localization Using Simulated Occlusion Datasets. IEEE Sensors Letters, 5(3), 1–4. https://doi.org/10.1109/LSENS.2021.3060097</mixed-citation>
                    </ref>
                                    <ref id="ref62">
                        <label>62</label>
                        <mixed-citation publication-type="journal">Huang, H.-C., Xu, S. S.-D., Lin, H.-C., Xiao, Y.-S., &amp; Chen, Y.-X. (2023). Design and Implementation of Intelligent LiDAR SLAM for Autonomous Mobile Robots Using Evolutionary Normal Distributions Transform. Soft Computing, 5321–5337. https://doi.org/10.1007/s00500-023-09219-0</mixed-citation>
                    </ref>
                                    <ref id="ref63">
                        <label>63</label>
                        <mixed-citation publication-type="journal">Chen, S., Ma, H., Jiang, C., Zhou, B., Xue, W., Xiao, Z., &amp; Li, Q. (2022). NDT-LOAM: A Real-Time Lidar Odometry and Mapping With Weighted NDT and LFA. IEEE Sensors Journal, 22(4), 3660–3671. https://doi.org/10.1109/JSEN.2021.3135055</mixed-citation>
                    </ref>
                                    <ref id="ref64">
                        <label>64</label>
                        <mixed-citation publication-type="journal">Bouraine, S. , Bougouffa, A. , &amp; Azouaoui, O. (2020). NDT-PSO, a New NDT based SLAM Approach using Particle Swarm Optimization. In 2020 16th International Conference on Control, Automation, Robotics and Vision (ICARCV) (pp. 321–326). Shenzhen, China: IEEE. https://doi.org/doi: 10.1109/ICARCV50220.2020.9305519.</mixed-citation>
                    </ref>
                                    <ref id="ref65">
                        <label>65</label>
                        <mixed-citation publication-type="journal">Feng, H. M. (2006). Self-Generation RBFNs using Evolutional PSO Learning. Neurocomputing, 70(1–3), 241–251. https://doi.org/10.1016/j.neucom.2006.03.007</mixed-citation>
                    </ref>
                                    <ref id="ref66">
                        <label>66</label>
                        <mixed-citation publication-type="journal">Jain, M., Saihjpal, V., Singh, N., &amp; Singh, S. B. (2022). An Overview of Variants and Advancements of PSO Algorithm. Applied Sciences, 12(17), 8392. https://doi.org/10.3390/app12178392</mixed-citation>
                    </ref>
                                    <ref id="ref67">
                        <label>67</label>
                        <mixed-citation publication-type="journal">Yakar, M.; Yılmaz, H.M.; Mutluoǧlu, Ö. Comparative evaluation of excavation volume by TLS and total topographic station based methods. Lasers Eng. 2010, 19, 331–345.</mixed-citation>
                    </ref>
                                    <ref id="ref68">
                        <label>68</label>
                        <mixed-citation publication-type="journal">Yakar, M., Yilmaz, H. M., &amp; Mutluoglu, O. (2014). Performance of photogrammetric and terrestrial laser scanning methods in volume computing of excavtion and filling areas. Arabian Journal for Science and Engineering, 39, 387-394.</mixed-citation>
                    </ref>
                            </ref-list>
                    </back>
    </article>
