Araştırma Makalesi

Localization and Point Cloud Based 3D Mapping with Autonomous Robots

31 Ekim 2019
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Localization and Point Cloud Based 3D Mapping with Autonomous Robots

Öz

In this study, localization and environment mapping application is aimed with an autonomous robot. A new algorithm is presented to scan a larger area, to produce faster and more accurate results. The mapping process is intended not to be affected by environmental movements. It can be used in military areas to gain manpower in the mine area or to model the environment in virtual reality applications. In autonomous robot design, the horizontal and vertical angle values of the Lidar Lite V3 are provided by two servo motors. A four-wheeled car model was used. Ultrasonic sensors are placed on the front, right and left surfaces of the robot, Raspberry Pi 3 and Pi Camera was placed on top. It is seen that the moving average filter removes the noise generated on the map. The Lidar Lite V3 was able to take measurements at longer distances. Noise generation is prevented by motion detection algorithm. It can be used in interior space mapping, environment modeling, virtual reality applications, military areas, mining sector and graphic applications. In outdoor mapping, it can be used to create a map of an area of 40 meters in diameter. The mapping process was performed as close to the actual values by using the moving average filter and the Lidar Lite V3. The mapping process with the motion detection system is paused and actual position data are obtained using GPS. 

Anahtar Kelimeler

Kaynakça

  1. Açıkel, S. & Gökçen A. (2018). Two-dimensional environmental mapping and route tracking by using lidar in otonom robots. IV. INES Internatıonal Academic Research Congress (INES - 2018), Antalya.
  2. Altuntaş, N., Uslu, E., Çakmak, F., Amasyalı, M. F., & Yavuz, S. (2017, October). Comparison of 3-dimensional SLAM systems: RTAB-Map vs. Kintinuous. In Computer Science and Engineering (UBMK), 2017 International Conference on (pp. 99-103). IEEE.
  3. Ankışhan, H., & Efe, M. (2010). Kalman filter approaches for simultaneous localization and mapping. DÜMF Engineering Journal, 1(1), 13-20.
  4. Bradski, G., & Kaehler, A. (2008). Learning OpenCV: Computer vision with the OpenCV library. "O'Reilly Media, Inc.",(pp. 265-271)
  5. Carlone, L., Tron, R., Daniilidis, K., & Dellaert, F. (2015, May). Initialization techniques for 3D SLAM: a survey on rotation estimation and its use in pose graph optimization. In Robotics and Automation (ICRA), 2015 IEEE International Conference on (pp. 4597-4604). IEEE.
  6. Dissanayake, M. G., Newman, P., Durrant-Whyte, H. F., Clark, S., & Csorba, M. (2000). An experimental and theoretical investigation into simultaneous localisation and map building. In Experimental robotics VI (pp. 265-274). Springer, London.
  7. Durrant-Whyte, H., & Bailey, T. (2006). Simultaneous localization and mapping: part I. IEEE robotics & automation magazine, 13(2), 99-110. Fowler, R. A. (2000). The lowdown on LIDAR. Earth Observation Magazine, 9(3), 5.
  8. Golestan, S., Ramezani, M., Guerrero, J. M., Freijedo, F. D., & Monfared, M. (2013). Moving average filter based phase-locked loops: Performance analysis and design guidelines. IEEE Transactions on Power Electronics, 29(6), 2750-2763.

Ayrıntılar

Birincil Dil

İngilizce

Konular

-

Bölüm

Araştırma Makalesi

Yayımlanma Tarihi

31 Ekim 2019

Gönderilme Tarihi

1 Ağustos 2019

Kabul Tarihi

22 Ekim 2019

Yayımlandığı Sayı

Yıl 2019

Kaynak Göster

APA
Açıkel, S., & Gökçen, A. (2019). Localization and Point Cloud Based 3D Mapping with Autonomous Robots. Avrupa Bilim ve Teknoloji Dergisi, 82-92. https://doi.org/10.31590/ejosat.636389
AMA
1.Açıkel S, Gökçen A. Localization and Point Cloud Based 3D Mapping with Autonomous Robots. EJOSAT. Published online 01 Ekim 2019:82-92. doi:10.31590/ejosat.636389
Chicago
Açıkel, Selya, ve Ahmet Gökçen. 2019. “Localization and Point Cloud Based 3D Mapping with Autonomous Robots”. Avrupa Bilim ve Teknoloji Dergisi, Ekim 1, 82-92. https://doi.org/10.31590/ejosat.636389.
EndNote
Açıkel S, Gökçen A (01 Ekim 2019) Localization and Point Cloud Based 3D Mapping with Autonomous Robots. Avrupa Bilim ve Teknoloji Dergisi 82–92.
IEEE
[1]S. Açıkel ve A. Gökçen, “Localization and Point Cloud Based 3D Mapping with Autonomous Robots”, EJOSAT, ss. 82–92, Eki. 2019, doi: 10.31590/ejosat.636389.
ISNAD
Açıkel, Selya - Gökçen, Ahmet. “Localization and Point Cloud Based 3D Mapping with Autonomous Robots”. Avrupa Bilim ve Teknoloji Dergisi. 01 Ekim 2019. 82-92. https://doi.org/10.31590/ejosat.636389.
JAMA
1.Açıkel S, Gökçen A. Localization and Point Cloud Based 3D Mapping with Autonomous Robots. EJOSAT. 2019;:82–92.
MLA
Açıkel, Selya, ve Ahmet Gökçen. “Localization and Point Cloud Based 3D Mapping with Autonomous Robots”. Avrupa Bilim ve Teknoloji Dergisi, Ekim 2019, ss. 82-92, doi:10.31590/ejosat.636389.
Vancouver
1.Selya Açıkel, Ahmet Gökçen. Localization and Point Cloud Based 3D Mapping with Autonomous Robots. EJOSAT. 01 Ekim 2019;82-9. doi:10.31590/ejosat.636389