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Otonom Robotlarla Lokalizasyon ve Nokta Bulutu Tabanlı 3B Haritalama

Year 2019, Special Issue 2019, 82 - 92, 31.10.2019
https://doi.org/10.31590/ejosat.636389

Abstract

Bu çalışmada otonom bir robot ile çevre haritalaması ve konum takibi
yapılması amaçlanmıştır. Daha geniş bir alanın taranması, daha hızlı ve doğru
sonuçların üretilmesi amacıyla yeni bir algoritma sunulmuştur. Haritalama
işleminin ortam hareketlerinden etkilenmemesi amaçlanmıştır. Askeri alanlarda,
maden alanında insan gücünden kazanç sağlamak veya sanal gerçeklik
uygulamalarında ortam modeli çıkarmak amacıyla kullanılabilmektedir. Otonom
robot tasarımında iki adet servo motor ile Lidar Lite V3’e yatay ve düşey açı
değerleri verilmiştir. Dört tekerlekli bir araba modeli kullanılmıştır. Robotun
ön, sağ ve sol yüzeylerine birer ultrasonik sensör ve üzerine Raspberry Pi 3
yerleştirilmiştir. Hareketli ortalamalar filtresinin haritada oluşan
gürültüleri giderdiği görülmüştür. Lidar Lite V3 ile daha uzak mesafelerden
ölçüm alınabilmiştir. Hareket algılama algoritması sayesinde gürültü oluşumu
engellenmiştir. Pratikte iç mekan haritalamada, ortam modellemede, sanal
gerçeklik uygulamalarında, askeri alanlarda, maden sektöründe ve grafik
uygulamalarında kullanılabilir. Dış mekan haritalamada ise kırk metre çapında
bir alanın haritasını oluşturmakta kullanılabilir. Haritalama işlemi, hareketli
ortalamalar filtresi ve Lidar Lite V3 kullanılarak gerçek değerlere en yakın
şekilde gerçekleştirilmiştir. Hareket algılama sistemi ile haritalama işlemi
duraklatılmıştır ve GPS kullanılarak gerçek konum verileri elde edilmiştir.

References

  • 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.
  • 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.
  • Ankışhan, H., & Efe, M. (2010). Kalman filter approaches for simultaneous localization and mapping. DÜMF Engineering Journal, 1(1), 13-20.
  • Bradski, G., & Kaehler, A. (2008). Learning OpenCV: Computer vision with the OpenCV library. "O'Reilly Media, Inc.",(pp. 265-271)
  • 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.
  • 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.
  • 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.
  • 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.
  • Graff, K. F. (1981). A history of ultrasonics. In Physical acoustics (Vol. 15, pp. 1-97). Academic Press. Hosseinzadeh, M., Li, K., Latif, Y., & Reid, I. (2018). Real-Time Monocular Object-Model Aware Sparse SLAM. arXiv preprint arXiv:1809.09149.
  • Jury, E. I. (1964). Theory and Application of the z-Transform Method (pp. 176-179).
  • Kurt, Z. (2007). Development of intelligent algorithms for simultaneous positioning and mapping. (Doctoral dissertation, Yıldız Technical University, Institute of Science and Technology, Istanbul).
  • Lee, K., Ryu, S. H., Nam, C., & Doh, N. L. (2018). A practical 2D/3D SLAM using directional patterns of an indoor structure. Intelligent Service Robotics, 11(1), 1-24.
  • Maulana, I., Rusdinar, A., & Priramadhi, R. A. (2018). Application of Lidar for Mapping and Navigation in Closed Environments. eProceedings of Engineering, 5(1).
  • Ramli, M. F. B., Shamsudin, S. S., & Legowo, A. (2017). Obstacle Detection Technique Using Multi Sensor Integration for Small Unmanned Aerial Vehicle. Indonesian Journal of Electrical Engineering and Computer Science, 8(2), 441-449.
  • Rusu, R. B., Marton, Z. C., Blodow, N., Dolha, M., & Beetz, M. (2008). Towards 3D point cloud based object maps for household environments. Robotics and Autonomous Systems, 56(11), 927-941.
  • Siciliano, B., Sciavicco, L., Villani, L., & Oriolo, G. (2010). Robotics: modelling, planning and control. Springer Science & Business Media.
  • Thrun, S. (2002). Robotic mapping: A survey. Exploring artificial intelligence in the new millennium, 1(1-35), 1.
  • Üstün, A. (1996). Datum conversions. (Master Thesis, Yıldız Technical University, Institute of Science and Technology, Istanbul).

Localization and Point Cloud Based 3D Mapping with Autonomous Robots

Year 2019, Special Issue 2019, 82 - 92, 31.10.2019
https://doi.org/10.31590/ejosat.636389

Abstract

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. 

References

  • 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.
  • 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.
  • Ankışhan, H., & Efe, M. (2010). Kalman filter approaches for simultaneous localization and mapping. DÜMF Engineering Journal, 1(1), 13-20.
  • Bradski, G., & Kaehler, A. (2008). Learning OpenCV: Computer vision with the OpenCV library. "O'Reilly Media, Inc.",(pp. 265-271)
  • 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.
  • 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.
  • 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.
  • 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.
  • Graff, K. F. (1981). A history of ultrasonics. In Physical acoustics (Vol. 15, pp. 1-97). Academic Press. Hosseinzadeh, M., Li, K., Latif, Y., & Reid, I. (2018). Real-Time Monocular Object-Model Aware Sparse SLAM. arXiv preprint arXiv:1809.09149.
  • Jury, E. I. (1964). Theory and Application of the z-Transform Method (pp. 176-179).
  • Kurt, Z. (2007). Development of intelligent algorithms for simultaneous positioning and mapping. (Doctoral dissertation, Yıldız Technical University, Institute of Science and Technology, Istanbul).
  • Lee, K., Ryu, S. H., Nam, C., & Doh, N. L. (2018). A practical 2D/3D SLAM using directional patterns of an indoor structure. Intelligent Service Robotics, 11(1), 1-24.
  • Maulana, I., Rusdinar, A., & Priramadhi, R. A. (2018). Application of Lidar for Mapping and Navigation in Closed Environments. eProceedings of Engineering, 5(1).
  • Ramli, M. F. B., Shamsudin, S. S., & Legowo, A. (2017). Obstacle Detection Technique Using Multi Sensor Integration for Small Unmanned Aerial Vehicle. Indonesian Journal of Electrical Engineering and Computer Science, 8(2), 441-449.
  • Rusu, R. B., Marton, Z. C., Blodow, N., Dolha, M., & Beetz, M. (2008). Towards 3D point cloud based object maps for household environments. Robotics and Autonomous Systems, 56(11), 927-941.
  • Siciliano, B., Sciavicco, L., Villani, L., & Oriolo, G. (2010). Robotics: modelling, planning and control. Springer Science & Business Media.
  • Thrun, S. (2002). Robotic mapping: A survey. Exploring artificial intelligence in the new millennium, 1(1-35), 1.
  • Üstün, A. (1996). Datum conversions. (Master Thesis, Yıldız Technical University, Institute of Science and Technology, Istanbul).
There are 18 citations in total.

Details

Primary Language English
Journal Section Articles
Authors

Selya Açıkel 0000-0001-5443-6713

Ahmet Gökçen This is me 0000-0002-7569-5447

Publication Date October 31, 2019
Published in Issue Year 2019 Special Issue 2019

Cite

APA Açıkel, S., & Gökçen, A. (2019). Localization and Point Cloud Based 3D Mapping with Autonomous Robots. Avrupa Bilim Ve Teknoloji Dergisi82-92. https://doi.org/10.31590/ejosat.636389