Araştırma Makalesi
BibTex RIS Kaynak Göster
Yıl 2019, Cilt: 23 Sayı: 4, 617 - 632, 01.08.2019
https://doi.org/10.16984/saufenbilder.453926

Öz

Kaynakça

  • Link, J. A. B., Smith, P., Viol, N., & Wehrle, K. (2011, September). Footpath: Accurate map-based indoor navigation using smartphones. In Indoor Positioning and Indoor Navigation (IPIN), 2011 International Conference on (pp. 1-8). IEEE.
  • Hilsenbeck, S., Bobkov, D., Schroth, G., Huitl, R., & Steinbach, E. (2014, September). Graph-based data fusion of pedometer and WiFi measurements for mobile indoor positioning. In Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing (pp. 147-158). ACM.
  • Negenborn, R. (2003). Robot localization and Kalman filters (Doctoral dissertation, Utrecht University).
  • Lee, B. G., & Chung, W. Y. (2011). Multitarget three-dimensional indoor navigation on a PDA in a wireless sensor network. IEEE Sensors Journal, 11(3), 799-807.
  • Fox, D., Burgard, W., Dellaert, F., & Thrun, S. (1999). Monte carlo localization: Efficient position estimation for mobile robots. AAAI/IAAI, 1999(343-349), 2-2.
  • Zhao, H., & Wang, Z. (2012). Motion measurement using inertial sensors, ultrasonic sensors, and magnetometers with extended kalman filter for data fusion. IEEE Sensors Journal, 12(5), 943-953.
  • Fu, G., Zhang, J., Chen, W., Peng, F., Yang, P., & Chen, C. (2013). Precise localization of mobile robots via odometry and wireless sensor network. International Journal of Advanced Robotic Systems, 10(4), 203.
  • Biswas, J., & Veloso, M. (2010, May). Wifi localization and navigation for autonomous indoor mobile robots. In Robotics and Automation (ICRA), 2010 IEEE International Conference on (pp. 4379-4384). IEEE.
  • Ni, L. M., Liu, Y., Lau, Y. C., & Patil, A. P. (2004). LANDMARC: indoor location sensing using active RFID. Wireless networks, 10(6), 701-710.
  • Kiers, M., Bischof, W., Krajnc, E., & Dornhofer, M. (2011, September). Evaluation and improvements of an rfid based indoor navigation system for visually impaired and blind people. In 2011 International Conference on Indoor Positioning and Indoor Navigation; Paper, Guimarães, Portugal (Vol. 16).
  • Yenilmez, L., & Temelta, H. Mobil Robotlarda Ultrasonik Duyaçlarla Gerçek Zamanda Konum Kestirimi.
  • Fujimoto, M., Nakamori, E., Inada, A., Oda, Y., Wada, T., Mutsuura, K., & Okada, H. (2011, September). A broad-typed multi-sensing-range method for indoor position estimation of passive RFID tags. In Proceedings of the 2011 International Conference on Indoor Positioning and Indoor Navigation (IPIN'2011).
  • Han, S., Lim, H., & Lee, J. (2007). An efficient localization scheme for a differential-driving mobile robot based on RFID system. IEEE Transactions on Industrial Electronics, 54(6), 3362-3369.
  • Malu, K., & Jharna Majumdar, S. (2014). Kinematics, Localization and Control of Differential Drive Mobile Robot. Global Journal of Research In Engineering, 14(1).
  • Altun, K., & Barshan, B. (2012). Pedestrian dead reckoning employing simultaneous activity recognition cues. Measurement Science and Technology, 23(2), 025103.
  • Hamerlain, F., Floquet, T., & Perruquetti, W. (2014). Experimental tests of a sliding mode controller for trajectory tracking of a car-like mobile robot. Robotica, 32(01), 63-76.
  • Ojeda, L., & Borenstein, J. (2004). Methods for the reduction of odometry errors in over-constrained mobile robots. Autonomous Robots, 16(3), 273-286.
  • Korkmaz, H., Cosgun, E. Odometry error recovery by using a PID controller in the case of linear displacement of 4-wheeled indoor mapping robot (in Turkish) TOK2013- National Automatic Control Meeting and Exhibition. Malatya İnönü Üniversitesi, Türkiye, 2013 Sep.
  • Maimone, M., Cheng, Y., & Matthies, L. (2007). Two years of visual odometry on the mars exploration rovers. Journal of Field Robotics, 24(3), 169-186.
  • Kuramachi, R., Ohsato, A., Sasaki, Y., & Mizoguchi, H. (2015, December). G-ICP SLAM: An odometry-free 3D mapping system with robust 6DoF pose estimation. In Robotics and Biomimetics (ROBIO), 2015 IEEE International Conference on (pp. 176-181). IEEE.
  • Lang, H., Wang, Y., & de Silva, C. W. (2008, September). Mobile robot localization and object pose estimation using optical encoder, vision and laser sensors. In Automation and Logistics, 2008. ICAL 2008. IEEE International Conference on (pp. 617-622). IEEE.
  • Kottmeier, S., Müller, S., Schmidt, T., Zajac, K., & Schmalbach, M. (2011). A High-Precision, High-Reliability Binary Rotary Encoder Using Hall-Effect Sensors. Proc. Of the 14th ESMATS.
  • Niu, X., Yu, T., Tang, J., & Chang, L. (2017). An Online Solution of LiDAR Scan Matching Aided Inertial Navigation System for Indoor Mobile Mapping. Hindawi Publishing Corporation Mobile Information Systems.
  • W. Hess, D. Kohler, H. Rapp, and D. Andor, Real-Time Loop Closure in 2D LIDAR SLAM, in Robotics and Automation (ICRA), 2016 IEEE International Conference on. IEEE, 2016. pp. 1271–1278.
  • Esenkanova, J., İlhan, H. O., & Yavuz, S. (2013). Pre-Mapping system with single laser sensor based on gmapping algorithm. IJOEE: Int. Journal of Electrical Energy, 1(2), 97-101.
  • Eliazar, A., & Parr, R. (2003, August). DP-SLAM: Fast, robust simultaneous localization and mapping without predetermined landmarks. In IJCAI (Vol. 3, pp. 1135-1142).
  • Ponce-Cruz, P., Molina, A., & MacCleery, B. (2016). Fuzzy Logic Type 1 and Type 2 Based on LabVIEWTM FPGA (Vol. 334). Springer.
  • Orlowska-Kowalska, T., & Kaminski, M. (2011). FPGA implementation of the multilayer neural network for the speed estimation of the two-mass drive system. IEEE transactions on Industrial Informatics, 7(3), 436-445.
  • Gong, Z., Li, J., & Li, W. (2016, July). A low cost indoor mapping robot based on TinySLAM algorithm. In Geoscience and Remote Sensing Symposium (IGARSS), 2016 IEEE International (pp. 4549-4552). IEEE.
  • Borenstein, J., Everett, H. R., Feng, L., & Wehe, D. (1997). Mobile robot positioning-sensors and techniques. Naval Command Control And Ocean Surveillance Center Rdt And E Div San Diego Ca.
  • Fulmek, P. L., Wandling, F., Zdiarsky, W., Brasseur, G., & Cermak, S. P. (2002). Capacitive sensor for relative angle measurement. IEEE Transactions on Instrumentation and Measurement, 51(6), 1145-1149.
  • Hide, C., Moore, T., & Smith, M. (2004, April). Adaptive Kalman filtering algorithms for integrating GPS and low cost INS. In Position Location and Navigation Symposium, 2004. PLANS 2004 (pp. 227-233). IEEE.
  • Mirowski, P., Palaniappan, R., & Ho, T. K. (2012, April). Depth camera SLAM on a low-cost WiFi mapping robot. In Technologies for Practical Robot Applications (TePRA), 2012 IEEE International Conference on (pp. 1-6). IEEE.
  • Sileshi, B. G., Oliver, J., Toledo, R., Gonçalves, J., & Costa, P. (2016). On the behaviour of low cost laser scanners in HW/SW particle filter SLAM applications. Robotics and Autonomous Systems, 80, 11-23.
  • Kohlbrecher, S., Meyer, J., Graber, T., Petersen, K., Klingauf, U., & von Stryk, O. (2013, June). Hector Open Source Modules for Autonomous Mapping and Navigation with Rescue Robots. In RoboCup (pp. 624-631).
  • Szöcs, D., Pană, T., Feneşan, A., & Vese, I. (2012, October). Error and drift attenuation for wheel slip measurement of a prototype electric vehicle. In Electrical and Power Engineering (EPE), 2012 International Conference and Exposition on (pp. 64-69). IEEE.
  • Monmasson, E., Idkhajine, L., Cirstea, M. N., Bahri, I., Tisan, A., & Naouar, M. W. (2011). FPGAs in industrial control applications. IEEE Transactions on Industrial informatics, 7(2), 224-243.
  • Korkmaz, H., Cosgun, E., Bal, S. A., & Toker, K. (2014, April). Developing a graduate level embedded system programming course content by using blended programming methodologies: Text-based and graphical. In Global Engineering Education Conference (EDUCON), 2014 IEEE (pp. 1010-1015). IEEE.
  • Falcon, J. S., & Trimborn, M. (2006, June). Graphical programming for field programmable gate arrays: applications in control and mechatronics. In American Control Conference, 2006 (pp. 7-pp). IEEE.
  • Kamenar, E., & Zelenika, S. (2013, May). Micropositioning mechatronics system based on FPGA architecture. In Information & Communication Technology Electronics & Microelectronics (MIPRO), 2013 36th International Convention on (pp. 125-130). IEEE.
  • MacCleery, B., & Kassas, Z. M. (2008). New mechatronics development techniques for FPGA-based control and simulation of electromechanical systems. IFAC Proceedings Volumes, 41(2), 4434-4439.
  • NI White Papers, Graphical System Design Basics: Accelerating Development Time and Bringing Embedded Design to the Masses, Publish Date: Oct 19, 2012. http://www.ni.com/white-paper/3392/en/
  • Pololu Robotics and Electronics https://www.pololu.com/product/1217 (22.06.2017)
  • NI White Papers, Quadrature Encoder Example DAQ Personality, Publish Date: Aug 24, 2016. (22.06.2017)
  • Product manual, https://www.dfrobot.com/wiki/index.php/URM37_V4.0_Ultrasonic_Sensor_(SKU:SEN0001) (22.06.2017)
  • High-precision positioning inertial navigation system Product page, http://feiyutech.en.alibaba.com/product/528618009-801939455/FY_2000B_AHRS.html (22.06.2017)
  • Digi XBee® ZigBee Product page, “ Embedded ZigBee and Thread-ready RF modules provide OEMs with a simple way to integrate mesh technology into their application”, https://www.digi.com/products/xbee-rf-solutions/2-4-ghz-modules/xbee-zigbee (22.06.2017)

An Embedded System Design to Build Real-Time 2D Maps for Unknown Indoor Environments

Yıl 2019, Cilt: 23 Sayı: 4, 617 - 632, 01.08.2019
https://doi.org/10.16984/saufenbilder.453926

Öz

In this paper, a remotely controllable, differentially driven
wheeled mobile robot was developed in order to build 2D maps of unknown indoor
environments; this system would eliminate the need to pre-explore such
environments. Main aim of the study is to develop a system with high accuracy
by using minimum number of sensors and a processor with low cost especially for
comparatively small indoor areas. The distance traveled was calculated using
the wheel odometry method. Obstacles surrounding the robot, the distance
traveled, and the robot’s orientation were obtained using an ultrasonic
distance sensor, optical encoder, and a 3D orientation sensor (also known as an
Attitude and Heading Reference System –AHRS), respectively. In addition, the
characteristics of the system hardware components were empirically explored,
and the errors resulting from the sensors were evaluated. The non-linearity
percentage error arising from the encoder was defined and then compensated for.
The hysteresis behavior of the ultrasonic distance sensors was also empirically
tested. All of the tasks were conducted by using a low-cost FPGA (Field
Programmable Gate Arrays) board. This study used the graphical development
platform, National Instruments (NI) LabVIEW, and it’s FPGA Module which is used
for programming of embedded systems instead of the text-based HDLs (Hardware
Description Languages). This distinguishes the proposed system from similar
prior studies.

Kaynakça

  • Link, J. A. B., Smith, P., Viol, N., & Wehrle, K. (2011, September). Footpath: Accurate map-based indoor navigation using smartphones. In Indoor Positioning and Indoor Navigation (IPIN), 2011 International Conference on (pp. 1-8). IEEE.
  • Hilsenbeck, S., Bobkov, D., Schroth, G., Huitl, R., & Steinbach, E. (2014, September). Graph-based data fusion of pedometer and WiFi measurements for mobile indoor positioning. In Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing (pp. 147-158). ACM.
  • Negenborn, R. (2003). Robot localization and Kalman filters (Doctoral dissertation, Utrecht University).
  • Lee, B. G., & Chung, W. Y. (2011). Multitarget three-dimensional indoor navigation on a PDA in a wireless sensor network. IEEE Sensors Journal, 11(3), 799-807.
  • Fox, D., Burgard, W., Dellaert, F., & Thrun, S. (1999). Monte carlo localization: Efficient position estimation for mobile robots. AAAI/IAAI, 1999(343-349), 2-2.
  • Zhao, H., & Wang, Z. (2012). Motion measurement using inertial sensors, ultrasonic sensors, and magnetometers with extended kalman filter for data fusion. IEEE Sensors Journal, 12(5), 943-953.
  • Fu, G., Zhang, J., Chen, W., Peng, F., Yang, P., & Chen, C. (2013). Precise localization of mobile robots via odometry and wireless sensor network. International Journal of Advanced Robotic Systems, 10(4), 203.
  • Biswas, J., & Veloso, M. (2010, May). Wifi localization and navigation for autonomous indoor mobile robots. In Robotics and Automation (ICRA), 2010 IEEE International Conference on (pp. 4379-4384). IEEE.
  • Ni, L. M., Liu, Y., Lau, Y. C., & Patil, A. P. (2004). LANDMARC: indoor location sensing using active RFID. Wireless networks, 10(6), 701-710.
  • Kiers, M., Bischof, W., Krajnc, E., & Dornhofer, M. (2011, September). Evaluation and improvements of an rfid based indoor navigation system for visually impaired and blind people. In 2011 International Conference on Indoor Positioning and Indoor Navigation; Paper, Guimarães, Portugal (Vol. 16).
  • Yenilmez, L., & Temelta, H. Mobil Robotlarda Ultrasonik Duyaçlarla Gerçek Zamanda Konum Kestirimi.
  • Fujimoto, M., Nakamori, E., Inada, A., Oda, Y., Wada, T., Mutsuura, K., & Okada, H. (2011, September). A broad-typed multi-sensing-range method for indoor position estimation of passive RFID tags. In Proceedings of the 2011 International Conference on Indoor Positioning and Indoor Navigation (IPIN'2011).
  • Han, S., Lim, H., & Lee, J. (2007). An efficient localization scheme for a differential-driving mobile robot based on RFID system. IEEE Transactions on Industrial Electronics, 54(6), 3362-3369.
  • Malu, K., & Jharna Majumdar, S. (2014). Kinematics, Localization and Control of Differential Drive Mobile Robot. Global Journal of Research In Engineering, 14(1).
  • Altun, K., & Barshan, B. (2012). Pedestrian dead reckoning employing simultaneous activity recognition cues. Measurement Science and Technology, 23(2), 025103.
  • Hamerlain, F., Floquet, T., & Perruquetti, W. (2014). Experimental tests of a sliding mode controller for trajectory tracking of a car-like mobile robot. Robotica, 32(01), 63-76.
  • Ojeda, L., & Borenstein, J. (2004). Methods for the reduction of odometry errors in over-constrained mobile robots. Autonomous Robots, 16(3), 273-286.
  • Korkmaz, H., Cosgun, E. Odometry error recovery by using a PID controller in the case of linear displacement of 4-wheeled indoor mapping robot (in Turkish) TOK2013- National Automatic Control Meeting and Exhibition. Malatya İnönü Üniversitesi, Türkiye, 2013 Sep.
  • Maimone, M., Cheng, Y., & Matthies, L. (2007). Two years of visual odometry on the mars exploration rovers. Journal of Field Robotics, 24(3), 169-186.
  • Kuramachi, R., Ohsato, A., Sasaki, Y., & Mizoguchi, H. (2015, December). G-ICP SLAM: An odometry-free 3D mapping system with robust 6DoF pose estimation. In Robotics and Biomimetics (ROBIO), 2015 IEEE International Conference on (pp. 176-181). IEEE.
  • Lang, H., Wang, Y., & de Silva, C. W. (2008, September). Mobile robot localization and object pose estimation using optical encoder, vision and laser sensors. In Automation and Logistics, 2008. ICAL 2008. IEEE International Conference on (pp. 617-622). IEEE.
  • Kottmeier, S., Müller, S., Schmidt, T., Zajac, K., & Schmalbach, M. (2011). A High-Precision, High-Reliability Binary Rotary Encoder Using Hall-Effect Sensors. Proc. Of the 14th ESMATS.
  • Niu, X., Yu, T., Tang, J., & Chang, L. (2017). An Online Solution of LiDAR Scan Matching Aided Inertial Navigation System for Indoor Mobile Mapping. Hindawi Publishing Corporation Mobile Information Systems.
  • W. Hess, D. Kohler, H. Rapp, and D. Andor, Real-Time Loop Closure in 2D LIDAR SLAM, in Robotics and Automation (ICRA), 2016 IEEE International Conference on. IEEE, 2016. pp. 1271–1278.
  • Esenkanova, J., İlhan, H. O., & Yavuz, S. (2013). Pre-Mapping system with single laser sensor based on gmapping algorithm. IJOEE: Int. Journal of Electrical Energy, 1(2), 97-101.
  • Eliazar, A., & Parr, R. (2003, August). DP-SLAM: Fast, robust simultaneous localization and mapping without predetermined landmarks. In IJCAI (Vol. 3, pp. 1135-1142).
  • Ponce-Cruz, P., Molina, A., & MacCleery, B. (2016). Fuzzy Logic Type 1 and Type 2 Based on LabVIEWTM FPGA (Vol. 334). Springer.
  • Orlowska-Kowalska, T., & Kaminski, M. (2011). FPGA implementation of the multilayer neural network for the speed estimation of the two-mass drive system. IEEE transactions on Industrial Informatics, 7(3), 436-445.
  • Gong, Z., Li, J., & Li, W. (2016, July). A low cost indoor mapping robot based on TinySLAM algorithm. In Geoscience and Remote Sensing Symposium (IGARSS), 2016 IEEE International (pp. 4549-4552). IEEE.
  • Borenstein, J., Everett, H. R., Feng, L., & Wehe, D. (1997). Mobile robot positioning-sensors and techniques. Naval Command Control And Ocean Surveillance Center Rdt And E Div San Diego Ca.
  • Fulmek, P. L., Wandling, F., Zdiarsky, W., Brasseur, G., & Cermak, S. P. (2002). Capacitive sensor for relative angle measurement. IEEE Transactions on Instrumentation and Measurement, 51(6), 1145-1149.
  • Hide, C., Moore, T., & Smith, M. (2004, April). Adaptive Kalman filtering algorithms for integrating GPS and low cost INS. In Position Location and Navigation Symposium, 2004. PLANS 2004 (pp. 227-233). IEEE.
  • Mirowski, P., Palaniappan, R., & Ho, T. K. (2012, April). Depth camera SLAM on a low-cost WiFi mapping robot. In Technologies for Practical Robot Applications (TePRA), 2012 IEEE International Conference on (pp. 1-6). IEEE.
  • Sileshi, B. G., Oliver, J., Toledo, R., Gonçalves, J., & Costa, P. (2016). On the behaviour of low cost laser scanners in HW/SW particle filter SLAM applications. Robotics and Autonomous Systems, 80, 11-23.
  • Kohlbrecher, S., Meyer, J., Graber, T., Petersen, K., Klingauf, U., & von Stryk, O. (2013, June). Hector Open Source Modules for Autonomous Mapping and Navigation with Rescue Robots. In RoboCup (pp. 624-631).
  • Szöcs, D., Pană, T., Feneşan, A., & Vese, I. (2012, October). Error and drift attenuation for wheel slip measurement of a prototype electric vehicle. In Electrical and Power Engineering (EPE), 2012 International Conference and Exposition on (pp. 64-69). IEEE.
  • Monmasson, E., Idkhajine, L., Cirstea, M. N., Bahri, I., Tisan, A., & Naouar, M. W. (2011). FPGAs in industrial control applications. IEEE Transactions on Industrial informatics, 7(2), 224-243.
  • Korkmaz, H., Cosgun, E., Bal, S. A., & Toker, K. (2014, April). Developing a graduate level embedded system programming course content by using blended programming methodologies: Text-based and graphical. In Global Engineering Education Conference (EDUCON), 2014 IEEE (pp. 1010-1015). IEEE.
  • Falcon, J. S., & Trimborn, M. (2006, June). Graphical programming for field programmable gate arrays: applications in control and mechatronics. In American Control Conference, 2006 (pp. 7-pp). IEEE.
  • Kamenar, E., & Zelenika, S. (2013, May). Micropositioning mechatronics system based on FPGA architecture. In Information & Communication Technology Electronics & Microelectronics (MIPRO), 2013 36th International Convention on (pp. 125-130). IEEE.
  • MacCleery, B., & Kassas, Z. M. (2008). New mechatronics development techniques for FPGA-based control and simulation of electromechanical systems. IFAC Proceedings Volumes, 41(2), 4434-4439.
  • NI White Papers, Graphical System Design Basics: Accelerating Development Time and Bringing Embedded Design to the Masses, Publish Date: Oct 19, 2012. http://www.ni.com/white-paper/3392/en/
  • Pololu Robotics and Electronics https://www.pololu.com/product/1217 (22.06.2017)
  • NI White Papers, Quadrature Encoder Example DAQ Personality, Publish Date: Aug 24, 2016. (22.06.2017)
  • Product manual, https://www.dfrobot.com/wiki/index.php/URM37_V4.0_Ultrasonic_Sensor_(SKU:SEN0001) (22.06.2017)
  • High-precision positioning inertial navigation system Product page, http://feiyutech.en.alibaba.com/product/528618009-801939455/FY_2000B_AHRS.html (22.06.2017)
  • Digi XBee® ZigBee Product page, “ Embedded ZigBee and Thread-ready RF modules provide OEMs with a simple way to integrate mesh technology into their application”, https://www.digi.com/products/xbee-rf-solutions/2-4-ghz-modules/xbee-zigbee (22.06.2017)
Toplam 47 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Elektrik Mühendisliği
Bölüm Araştırma Makalesi
Yazarlar

Ercan Coşgun 0000-0003-4387-3699

Hayriye Korkmaz 0000-0002-5994-7587

Kenan Toker Bu kişi benim 0000-0001-8568-4277

Yayımlanma Tarihi 1 Ağustos 2019
Gönderilme Tarihi 16 Ağustos 2018
Kabul Tarihi 13 Şubat 2019
Yayımlandığı Sayı Yıl 2019 Cilt: 23 Sayı: 4

Kaynak Göster

APA Coşgun, E., Korkmaz, H., & Toker, K. (2019). An Embedded System Design to Build Real-Time 2D Maps for Unknown Indoor Environments. Sakarya University Journal of Science, 23(4), 617-632. https://doi.org/10.16984/saufenbilder.453926
AMA Coşgun E, Korkmaz H, Toker K. An Embedded System Design to Build Real-Time 2D Maps for Unknown Indoor Environments. SAUJS. Ağustos 2019;23(4):617-632. doi:10.16984/saufenbilder.453926
Chicago Coşgun, Ercan, Hayriye Korkmaz, ve Kenan Toker. “An Embedded System Design to Build Real-Time 2D Maps for Unknown Indoor Environments”. Sakarya University Journal of Science 23, sy. 4 (Ağustos 2019): 617-32. https://doi.org/10.16984/saufenbilder.453926.
EndNote Coşgun E, Korkmaz H, Toker K (01 Ağustos 2019) An Embedded System Design to Build Real-Time 2D Maps for Unknown Indoor Environments. Sakarya University Journal of Science 23 4 617–632.
IEEE E. Coşgun, H. Korkmaz, ve K. Toker, “An Embedded System Design to Build Real-Time 2D Maps for Unknown Indoor Environments”, SAUJS, c. 23, sy. 4, ss. 617–632, 2019, doi: 10.16984/saufenbilder.453926.
ISNAD Coşgun, Ercan vd. “An Embedded System Design to Build Real-Time 2D Maps for Unknown Indoor Environments”. Sakarya University Journal of Science 23/4 (Ağustos 2019), 617-632. https://doi.org/10.16984/saufenbilder.453926.
JAMA Coşgun E, Korkmaz H, Toker K. An Embedded System Design to Build Real-Time 2D Maps for Unknown Indoor Environments. SAUJS. 2019;23:617–632.
MLA Coşgun, Ercan vd. “An Embedded System Design to Build Real-Time 2D Maps for Unknown Indoor Environments”. Sakarya University Journal of Science, c. 23, sy. 4, 2019, ss. 617-32, doi:10.16984/saufenbilder.453926.
Vancouver Coşgun E, Korkmaz H, Toker K. An Embedded System Design to Build Real-Time 2D Maps for Unknown Indoor Environments. SAUJS. 2019;23(4):617-32.

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