Research Article
BibTex RIS Cite

NOKTA BULUTU VERİSİ İLE KURAL TABANLI AÇIK KAPI BULMA YÖNTEMİ

Year 2020, , 164 - 173, 31.08.2020

Burak Kaleci , Kaya Turgut

https://doi.org/10.31796/ogummf.723781
Cited By: 1

Abstract

Bina içi ortamlarda kapı konumlarının belirlenmesi problemi, konumlandırma, seyrüsefer, anlamsal sınıflandırma, yapı bilgi modellemesi ve otonom tekerlekli araç gibi çok farklı uygulamalarda ele alınmaktadır. Geçmiş çalışmalarda bu konu ile ilgili çözüm sunan yöntemler genellikle görsel bilgi ile kapalı kapıları ya da mesafe bilgisi ile açık kapıları belirlemeye çalışmışlardır. Son yıllarda nokta bulutu verisi ve/veya derinlik imgesi üretebilen algılayıcıların robotik alanında kullanılması ile birlikte bu veri kullanılarak kapı konumu belirlemeye çalışan yöntemler de geliştirilmiştir. Bu çalışmanın temel amacı bina içi ortamlarda nokta bulutu verisi kullanarak açık kapı konumunu gerçek zamanlı olarak tespit edebilecek bir yöntem sunmaktır. Bu yöntemde ilk olarak robotun yerel koordinat sistemine göre elde edilen nokta bulutu, küresel bir koordinat sistemine aktarılmaktadır. Daha sonra küresel koordinat sistemini temel alarak açık kapı konumunun tespiti için bir dizi kural tanımlamaktadır. Önerilen yöntemin verimliliğini ölçmek amacıyla GAZEBO benzetim ortamında, robotun açık kapı konumunu farklı açılardan gördüğü durumlar için elde edilen nokta bulutu verisi ile “OGUROB KAPI” veri kümesi oluşturulmuştur. Bu veri kümesi üzerinde yapılan testler kapı bulma başarısı ve süresi açısından incelenmiştir.Test sonuçları önerilen yöntem ile ortalama kapı bulma süresinin 10ms olduğunu göstermiştir. Ayrıca, kapı bulma doğru pozitif oranının %91 olduğu gözlemlenmiştir.

Keywords

Açık kapı bulma Nokta bulutu verisi Kural tabanlı GAZEBO

References

  • Bayram, K., Kolaylı, B., Solak, A., Tatar, B., Turgut, K. Ve Kaleci, B. (2019). 3B Nokta Bulutu Verisi ile Bölge Büyütme Tabanlı Kapı Bulma Uygulaması, Türkiye Robotbilim Konferansı, İstanbul, Turkiye, sayfa 139-145.
  • Bersan, D., Martins, R., Campos, M. ve Nascimento, E. R. (2018). Semantic Map Augmentation for Robot Navigation: A Learning Approach Based on Visual and Depth Data, Latin American Robotic Symposium, Brazilian Symposium on Robotics (SBR) and Workshop on Robotics in Education (WRE), Joao Pessoa, pp. 45-50.
  • Borgsen, S. M. Z., Schöpfer, M., Ziegler, L., ve Wachsmuth, S. (2014). Automated Door Detection with a 3D-Sensor, Canadian Conference on Computer and Robot Vision, Montreal, QC, 2014, pp. 276-282.
  • Derry, M., and Argall, B. (2013), Automated doorway detection for assistive shared-control wheelchairs, IEEE International Conference on Robotics and Automation, Karlsruhe, 2013, pp. 1254-1259.
  • Diaz-Vilarino, L., Khoshelham, K., Martinez-Sanchez, J., Arias, P. (2015). 3D modeling of building indoor spaces and closed doors from imagery and point clouds, Sensors (Basel) 15 (2) (2015) 3491–3512, https://doi.org/10.3390/s150203491.
  • Díaz-Vilariño, L., Verbree, E., Zlatanova, S., ve Diakité, A. (2017). Indoor modelling from SLAM-based laser scanner: Door detection to envelope reconstruction, Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. 2017;42:345–352. doi: 10.5194/isprs-archives-XLII-2-W7-345-2017.
  • ElKaissi, M., Elgamel, M., Bayoumi, M., ve Zavidovique, B. (2006). SEDLRF: A new door detectionsystem for topological maps, Computer Architecture for Machine Perception and Sensing, CAMP 2006, International Workshop on. IEEE.
  • Jung, J., Stachniss, C. Ju, S. ve Heo, J. (2018). Automated 3D volumetric reconstruction of multiple-room building interiors for as-built BIM, Adv. Eng. Inform. 38 811–825, https://doi.org/10.1016/j.aei.2018.10.007.
  • Gazebo, Robot Simulation Open source robotics foundation (OSRF) (2020). http://gazebosim.org/.
  • Goron, L. C., Tamas, L., ve Lazea, G. (2012). Classification within indoor environments using 3D perception, Proceedings of IEEE International Conference on Automation, Quality and Testing, Robotics, Cluj-Napoca, 2012, pp. 400-405.
  • Hensler J., Blaich M., Bittel O. (2010). Real-Time Door Detection Based on AdaBoost Learning Algorithm. In: Gottscheber A., Obdržálek D., Schmidt C. (eds) Research and Education in Robotics. Communications in Computer and Information Science, vol 82. Springer, Berlin, Heidelberg.
  • Kakillioglu, B., Ozcan, K., ve Velipasalar, S. (2016). Doorway detection for autonomous indoor navigation of unmanned vehicles, IEEE International Conference on Image Processing (ICIP), Phoenix, AZ, 2016, pp. 3837-3841.
  • Kaleci, B., Şenler, Ç. M., Dutagaci, H., ve Parlaktuna, O. (2015). Rule-Based Door Detection Using Laser Range Data in Indoor Environments, 2015 IEEE 27th International Conference on Tools with Artificial Intelligence (ICTAI), Vietri sul Mare, pp. 510-517.
  • Murillo, A. C., Košecká, J., Guerrero, J. J. ve Sagüés, C. (2008). Visual door detection integrating appearance and shape cues, Robotics and Autonomous Systems, 56(6), Elsevier, pages 512-521.
  • Pioneer P3-AT (2020). http://www.ist.tugraz.at/_attach/Publish/Kmr06/pioneer-robot.pdf.
  • Previtali, M., Díaz-Vilariño, L., ve Scaioni, M. (2018). Towars Automatic Reconstruction of Indoor Scenes from Incomplete Poınt Clouds: Door and Wındow Detectıon and Regularızatıon, International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences.
  • Quintana, B. Prieto, S. A., Adán, A. ve Bosché, F. (2018). Door detection in 3D coloured point clouds of indoor environments, In: Automation in Construction, Vol. 85. pp. 146-166.
  • Redmon, J., Divvala, S., Girshick, R., ve Farhadi, A. (2016). You only look once: Unified, real-time object detection, in IEEE CVPR.
  • Robot Operating System (ROS), Open source robotics foundation (OSRF) (2020). http://ros.org/.
  • Rusu, R. B., Meeussen, W., Chitta, S., ve Beetz, M. (2009). Laser-based perception for door and handle identification, International Conference on Advanced Robotics, Munich, pp. 1-8.
  • Rusu, R. B. ve Cousins, S. (2011). 3D is here: Point Cloud Library (PCL), 2011 IEEE International Conference on Robotics and Automation, Shanghai, pp. 1-4.
  • Staats, B., Diakité, A., Voûte, R., ve Zlatanova, S. (2019). Detection of doors in a voxel model, derived from a point cloud and its scanner trajectory, to improve the segmentation of the walkable space, International Journal of Urban Sciences, 23(3), 369-390.
  • Souto, L. A. V., Castro, A., Gonçalves, L. M. G., ve Nascimento, T. P. (2017). Stairs and Doors Recognition as Natural Landmarks Based on Clouds of 3D Edge-Points from RGB-D Sensors for Mobile Robot Localization, Sensors.
  • TS 9111- Özürlüler ve hareket kısıtlılığı bulunan kişiler için binalarda ulaşılabilirlik gerekleri, (2020). https://intweb.tse.org.tr/Standard/Standard/Standard.aspx?081118051115108051104119110104055047105102120088111043113104073097107103081101066102086100073109.
  • W. Meeussen, W., Wise, M., Glaser, S., ve Chitta., S. (2010). Autonomous door opening and plugging in with a personal robot, 2010 IEEE International Conference on Robotics and Automation, Anchorage, AK, 2010, pp. 729-736, doi: 10.1109/ROBOT.2010.5509556.
  • Xu, J., Kim, K., Zhang, L., ve Khosla, D. (2015). 3D Perception for Autonomous Robot Exploration, Advances in Visual Computing - 11th International SymposiumAt: Las Vegas, NV, USA.
  • Yang, X. ve Tian, Y. (2010). Robust door detection in unfamiliar environments by combining edge and corner features, Computer Vision and Pattern Recognition Workshops (CVPRW), IEEE Computer Society Conference on IEEE, pages 57-64.
  • Yuan, T. H., Hashim, F. H., Zaki W. M. D. W., ve Huddin, A. B. (2015). An automated 3D scanning algorithm using depth cameras for door detection, International Electronics Symposium (IES), Surabaya, pp. 58-61.

Year 2020, , 164 - 173, 31.08.2020

Burak Kaleci , Kaya Turgut

https://doi.org/10.31796/ogummf.723781
Cited By: 1

Abstract

References

  • Bayram, K., Kolaylı, B., Solak, A., Tatar, B., Turgut, K. Ve Kaleci, B. (2019). 3B Nokta Bulutu Verisi ile Bölge Büyütme Tabanlı Kapı Bulma Uygulaması, Türkiye Robotbilim Konferansı, İstanbul, Turkiye, sayfa 139-145.
  • Bersan, D., Martins, R., Campos, M. ve Nascimento, E. R. (2018). Semantic Map Augmentation for Robot Navigation: A Learning Approach Based on Visual and Depth Data, Latin American Robotic Symposium, Brazilian Symposium on Robotics (SBR) and Workshop on Robotics in Education (WRE), Joao Pessoa, pp. 45-50.
  • Borgsen, S. M. Z., Schöpfer, M., Ziegler, L., ve Wachsmuth, S. (2014). Automated Door Detection with a 3D-Sensor, Canadian Conference on Computer and Robot Vision, Montreal, QC, 2014, pp. 276-282.
  • Derry, M., and Argall, B. (2013), Automated doorway detection for assistive shared-control wheelchairs, IEEE International Conference on Robotics and Automation, Karlsruhe, 2013, pp. 1254-1259.
  • Diaz-Vilarino, L., Khoshelham, K., Martinez-Sanchez, J., Arias, P. (2015). 3D modeling of building indoor spaces and closed doors from imagery and point clouds, Sensors (Basel) 15 (2) (2015) 3491–3512, https://doi.org/10.3390/s150203491.
  • Díaz-Vilariño, L., Verbree, E., Zlatanova, S., ve Diakité, A. (2017). Indoor modelling from SLAM-based laser scanner: Door detection to envelope reconstruction, Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. 2017;42:345–352. doi: 10.5194/isprs-archives-XLII-2-W7-345-2017.
  • ElKaissi, M., Elgamel, M., Bayoumi, M., ve Zavidovique, B. (2006). SEDLRF: A new door detectionsystem for topological maps, Computer Architecture for Machine Perception and Sensing, CAMP 2006, International Workshop on. IEEE.
  • Jung, J., Stachniss, C. Ju, S. ve Heo, J. (2018). Automated 3D volumetric reconstruction of multiple-room building interiors for as-built BIM, Adv. Eng. Inform. 38 811–825, https://doi.org/10.1016/j.aei.2018.10.007.
  • Gazebo, Robot Simulation Open source robotics foundation (OSRF) (2020). http://gazebosim.org/.
  • Goron, L. C., Tamas, L., ve Lazea, G. (2012). Classification within indoor environments using 3D perception, Proceedings of IEEE International Conference on Automation, Quality and Testing, Robotics, Cluj-Napoca, 2012, pp. 400-405.
  • Hensler J., Blaich M., Bittel O. (2010). Real-Time Door Detection Based on AdaBoost Learning Algorithm. In: Gottscheber A., Obdržálek D., Schmidt C. (eds) Research and Education in Robotics. Communications in Computer and Information Science, vol 82. Springer, Berlin, Heidelberg.
  • Kakillioglu, B., Ozcan, K., ve Velipasalar, S. (2016). Doorway detection for autonomous indoor navigation of unmanned vehicles, IEEE International Conference on Image Processing (ICIP), Phoenix, AZ, 2016, pp. 3837-3841.
  • Kaleci, B., Şenler, Ç. M., Dutagaci, H., ve Parlaktuna, O. (2015). Rule-Based Door Detection Using Laser Range Data in Indoor Environments, 2015 IEEE 27th International Conference on Tools with Artificial Intelligence (ICTAI), Vietri sul Mare, pp. 510-517.
  • Murillo, A. C., Košecká, J., Guerrero, J. J. ve Sagüés, C. (2008). Visual door detection integrating appearance and shape cues, Robotics and Autonomous Systems, 56(6), Elsevier, pages 512-521.
  • Pioneer P3-AT (2020). http://www.ist.tugraz.at/_attach/Publish/Kmr06/pioneer-robot.pdf.
  • Previtali, M., Díaz-Vilariño, L., ve Scaioni, M. (2018). Towars Automatic Reconstruction of Indoor Scenes from Incomplete Poınt Clouds: Door and Wındow Detectıon and Regularızatıon, International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences.
  • Quintana, B. Prieto, S. A., Adán, A. ve Bosché, F. (2018). Door detection in 3D coloured point clouds of indoor environments, In: Automation in Construction, Vol. 85. pp. 146-166.
  • Redmon, J., Divvala, S., Girshick, R., ve Farhadi, A. (2016). You only look once: Unified, real-time object detection, in IEEE CVPR.
  • Robot Operating System (ROS), Open source robotics foundation (OSRF) (2020). http://ros.org/.
  • Rusu, R. B., Meeussen, W., Chitta, S., ve Beetz, M. (2009). Laser-based perception for door and handle identification, International Conference on Advanced Robotics, Munich, pp. 1-8.
  • Rusu, R. B. ve Cousins, S. (2011). 3D is here: Point Cloud Library (PCL), 2011 IEEE International Conference on Robotics and Automation, Shanghai, pp. 1-4.
  • Staats, B., Diakité, A., Voûte, R., ve Zlatanova, S. (2019). Detection of doors in a voxel model, derived from a point cloud and its scanner trajectory, to improve the segmentation of the walkable space, International Journal of Urban Sciences, 23(3), 369-390.
  • Souto, L. A. V., Castro, A., Gonçalves, L. M. G., ve Nascimento, T. P. (2017). Stairs and Doors Recognition as Natural Landmarks Based on Clouds of 3D Edge-Points from RGB-D Sensors for Mobile Robot Localization, Sensors.
  • TS 9111- Özürlüler ve hareket kısıtlılığı bulunan kişiler için binalarda ulaşılabilirlik gerekleri, (2020). https://intweb.tse.org.tr/Standard/Standard/Standard.aspx?081118051115108051104119110104055047105102120088111043113104073097107103081101066102086100073109.
  • W. Meeussen, W., Wise, M., Glaser, S., ve Chitta., S. (2010). Autonomous door opening and plugging in with a personal robot, 2010 IEEE International Conference on Robotics and Automation, Anchorage, AK, 2010, pp. 729-736, doi: 10.1109/ROBOT.2010.5509556.
  • Xu, J., Kim, K., Zhang, L., ve Khosla, D. (2015). 3D Perception for Autonomous Robot Exploration, Advances in Visual Computing - 11th International SymposiumAt: Las Vegas, NV, USA.
  • Yang, X. ve Tian, Y. (2010). Robust door detection in unfamiliar environments by combining edge and corner features, Computer Vision and Pattern Recognition Workshops (CVPRW), IEEE Computer Society Conference on IEEE, pages 57-64.
  • Yuan, T. H., Hashim, F. H., Zaki W. M. D. W., ve Huddin, A. B. (2015). An automated 3D scanning algorithm using depth cameras for door detection, International Electronics Symposium (IES), Surabaya, pp. 58-61.
There are 28 citations in total.

Details

Primary Language Turkish
Subjects Computer Software, Electrical Engineering
Journal Section Research Articles
Authors

Burak Kaleci 0000-0002-2001-3381

Kaya Turgut 0000-0003-3345-9339

Publication Date August 31, 2020
Acceptance Date July 19, 2020
Published in Issue Year 2020

Cite

APA Kaleci, B., & Turgut, K. (2020). NOKTA BULUTU VERİSİ İLE KURAL TABANLI AÇIK KAPI BULMA YÖNTEMİ. Eskişehir Osmangazi Üniversitesi Mühendislik Ve Mimarlık Fakültesi Dergisi, 28(2), 164-173. https://doi.org/10.31796/ogummf.723781
AMA Kaleci B, Turgut K. NOKTA BULUTU VERİSİ İLE KURAL TABANLI AÇIK KAPI BULMA YÖNTEMİ. ESOGÜ Müh Mim Fak Derg. August 2020;28(2):164-173. doi:10.31796/ogummf.723781
Chicago Kaleci, Burak, and Kaya Turgut. “NOKTA BULUTU VERİSİ İLE KURAL TABANLI AÇIK KAPI BULMA YÖNTEMİ”. Eskişehir Osmangazi Üniversitesi Mühendislik Ve Mimarlık Fakültesi Dergisi 28, no. 2 (August 2020): 164-73. https://doi.org/10.31796/ogummf.723781.
EndNote Kaleci B, Turgut K (August 1, 2020) NOKTA BULUTU VERİSİ İLE KURAL TABANLI AÇIK KAPI BULMA YÖNTEMİ. Eskişehir Osmangazi Üniversitesi Mühendislik ve Mimarlık Fakültesi Dergisi 28 2 164–173.
IEEE B. Kaleci and K. Turgut, “NOKTA BULUTU VERİSİ İLE KURAL TABANLI AÇIK KAPI BULMA YÖNTEMİ”, ESOGÜ Müh Mim Fak Derg, vol. 28, no. 2, pp. 164–173, 2020, doi: 10.31796/ogummf.723781.
ISNAD Kaleci, Burak - Turgut, Kaya. “NOKTA BULUTU VERİSİ İLE KURAL TABANLI AÇIK KAPI BULMA YÖNTEMİ”. Eskişehir Osmangazi Üniversitesi Mühendislik ve Mimarlık Fakültesi Dergisi 28/2 (August 2020), 164-173. https://doi.org/10.31796/ogummf.723781.
JAMA Kaleci B, Turgut K. NOKTA BULUTU VERİSİ İLE KURAL TABANLI AÇIK KAPI BULMA YÖNTEMİ. ESOGÜ Müh Mim Fak Derg. 2020;28:164–173.
MLA Kaleci, Burak and Kaya Turgut. “NOKTA BULUTU VERİSİ İLE KURAL TABANLI AÇIK KAPI BULMA YÖNTEMİ”. Eskişehir Osmangazi Üniversitesi Mühendislik Ve Mimarlık Fakültesi Dergisi, vol. 28, no. 2, 2020, pp. 164-73, doi:10.31796/ogummf.723781.
Vancouver Kaleci B, Turgut K. NOKTA BULUTU VERİSİ İLE KURAL TABANLI AÇIK KAPI BULMA YÖNTEMİ. ESOGÜ Müh Mim Fak Derg. 2020;28(2):164-73.

Cited By

COMPARISON OF DEEP LEARNING TECHNIQUES FOR DETECTION OF DOORS IN INDOOR ENVIRONMENTS
Eskişehir Osmangazi Üniversitesi Mühendislik ve Mimarlık Fakültesi Dergisi
https://doi.org/10.31796/ogummf.889095

20873 13565 13566 15461 13568  14913