DOI: https://doi.org/10.15587/2312-8372.2018.147861

Research of methods and technologies for determining the position of the mobile object in space

Olga Nechyporenko, Yaroslav Korpan

Abstract


The object of research is the process of tracking the position of a mobile object in space. One of the weakest points in tracking systems for the position of a mobile object in space is the problem of eliminating the ambiguity of determining key points when scanning the environment. This problem is especially important when several methods (or technologies) of position tracking are applied simultaneously. There is a need for additional calibration and adjustment.

The study used the results of the analysis of methods and technologies for automatically determining the position and orientation of three-dimensional objects using technical vision systems. Analysis of the considered popular systems and methods for measuring the spatial position of objects, as well as algorithms and navigation technologies of a mobile robot, has shown that each of the considered systems has its advantages and disadvantages. And it is used depending on the objectives of this system.

A comparative analysis of the main types of algorithms of the SLAM method has been carried out. The perspectives of this method – the use of artificial intelligence methods and an extended Kalman filter – improve the speed of the SLAM method. Proof of this is the huge number of open projects to create this type of navigation in various competitions:

  • VSLAM – implementation of the SLAM method based on computer vision methods;
  • RGBDSLAM – package for registering a cloud of points with RGBD sensors, such as Kinect or stereo cameras;
  • Hector_mapping – SLAM for platforms without odometer – only based on data from LIDAR, etc.
Since most modern technologies are increasingly using standardized formats of Wi-Fi, Bluetooth, GPS signals, it can be argued that using and analyzing information from a large number of sensors will increase the accuracy of determining the coordinates of an object several times. Creating the necessary information field of navigation and routing will allow to map and localize a mobile object on the ground with great accuracy

Keywords


SLAM method algorithms; positioning; mobile object; technical vision

References


Potapov, A. (2014). Sistemy komp'yuternogo zreniya: sovremennye zadachi i metody. Control Engineering, 1 (49), 20–26.

Newcombe, R. A., Lovegrove, S. J., Davison, A. J. (2011). DTAM: Dense tracking and mapping in real-time. IEEE International Conferenceon Computer Vision (ICCV). Barcelona, 2320–2327. doi: http://doi.org/10.1109/iccv.2011.6126513

Engel, J., Schöps, T., Cremers, D. (2014). LSD-SLAM: Large-Scale Direct Monocular SLAM. Lecture Notes in Computer Science. Cham, 834–849. doi: http://doi.org/10.1007/978-3-319-10605-2_54

Mur-Artal, R., Montiel, J. M. M., Tardos, J. D. (2015). ORB-SLAM: A Versatile and Accurate Monocular SLAM System. IEEE Transactions on Robotics, 31 (5), 1147–1163. doi: http://doi.org/10.1109/tro.2015.2463671

Werner, C., Werner, S., Schöne, R., Götz, S., Aßmann, U. (2018). Self-adaptive Synchronous Localization and Mapping using Runtime Feature Models. Proceedings of the 7th International Conference on Data Science, Technology and Applications, 1, 409–418. doi: http://doi.org/10.5220/0006945504090418

Nechyporenko, O. V., Korpan, Ya. V. (2016). Biometrychna identyfikatsiia i avtentyfikatsiia osoby za heometriieiu oblychchia. Visnyk Khmelnytskoho natsionalnoho universytetu, 4, 133–138.

Nechyporenko, O., Korpan, Y. (2017). Analysis of methods and technologies of human face recognition. Technology Audit and Production Reserves, 5 (2 (37)), 4–10. doi: http://doi.org/10.15587/2312-8372.2017.110868

Miroshnichenko, N. (2018). Mirovoy rynok AR dostignet ob"ema v 198 milliardov dollarov k 2025 godu. BIS Research. Novosti VR industrii. Available at: https://vrgeek.ru/mirovoj-rynok-ar-dostignet-obema-v-198-milliardov-dollarov-k-2025-godu/2018

Santos, F. M., Silva, V. F., Almeida, L. M. (2002). A robust self-localization system for a small mobile autonomous robot. International Symphosium on Robotics and Automation, 1–6. Available at: http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.134.2502

Antoni, D., Ban, Z., Zagar, M. (2001). Demining Robots – Requirements and Constraints. Automatika, 42 (3-4), 189–197.

Melo, L. F. de, Rosário, J. M., Junior, A. F. da S. (2013). Mobile Robot Indoor Autonomous Navigation with Position Estimation Using RF Signal Triangulation. Positioning, 4 (1), 20–35. doi: http://doi.org/10.4236/pos.2013.41004

Zakharov, A. A., Tuzhilkin, A. Yu., Vedenin, A. S. (2014). Algoritm opredeleniya polozheniya i orientatsii trekhmernykh obektov po videoizobrazheniyam na osnove veroyatnostnogo podkhoda. Fundamentalnye issledovaniya, 11-8, 1683–1687.

Menache, A. (2011). Understanding motion capture for computer animation. The Morgan Kaufmann Series In Computer Graphics, 254.

Tobon, R. (2010). The Mocap Book: A Practical Guide to the Art of Motion Capture. Forisforce, 258.

Nguyen, V., Harati, A., Siegwart, R. (2007). Lightweight SLAM algorithm using orthogonal planes for indoor mobile robotics. Intelligent Robots and Systems, 658–663. doi: http://doi.org/10.1109/iros.2007.4399512

Yuldashev, M. N. (2015). Ul'trazvukovye sistemy dlya opredeleniya prostranstvennogo polozheniya podvizhnogo obekta. Naukoemkie tekhnologii i intellektual'nye sistemy 2015. Moscow: MGTU im. N. E. Baumana, 465–472.

Nechyporenko, O. V., Korpan, Y. V., Nechyporenko, O. V., Khomchenko, O. S. (2018). Methods and technologies of monitoring of the position of a mobile object in space. Kompiuterne modeliuvannia ta optymizatsiia skladnykh system (KMOSS-2018). Dnipro: Balans-klub, 193–195.

Aulinas, J. (2008). The SLAM Problem: A Survey. Proceedings of the 2008 Conference on Artificial Intelligence Research & Development, 363–71. Available at: http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.163.6439


GOST Style Citations


Potapov A. Sistemy komp'yuternogo zreniya: sovremennye zadachi i metody // Control Engineering. 2014. Issue 1 (49). P. 20–26.

Newcombe R. A., Lovegrove S. J., Davison A. J. DTAM: Dense tracking and mapping in real-time // IEEE International Conferenceon Computer Vision (ICCV). Barcelona, 2011. P. 2320–2327. doi: http://doi.org/10.1109/iccv.2011.6126513 

Engel J., Schöps T., Cremers D. LSD-SLAM: Large-Scale Direct Monocular SLAM // Lecture Notes in Computer Science. Cham, 2014. P. 834–849. doi: http://doi.org/10.1007/978-3-319-10605-2_54 

Mur-Artal R., Montiel J. M. M., Tardos J. D. ORB-SLAM: A Versatile and Accurate Monocular SLAM System // IEEE Transactions on Robotics. 2015. Vol. 31, Issue 5. P. 1147–1163. doi: http://doi.org/10.1109/tro.2015.2463671 

Self-adaptive Synchronous Localization and Mapping using Runtime Feature Models / Werner C. et. al. // Proceedings of the 7th International Conference on Data Science, Technology and Applications. 2018. Vol. 1. P. 409–418. doi: http://doi.org/10.5220/0006945504090418 

Nechyporenko O. V., Korpan Ya. V. Biometrychna identyfikatsiia i avtentyfikatsiia osoby za heometriieiu oblychchia // Visnyk Khmelnytskoho natsionalnoho universytetu. 2016. Issue 4. P. 133–138.

Nechyporenko O., Korpan Y. Analysis of methods and technologies of human face recognition // Technology Audit and Production Reserves. 2017. Vol. 5, Issue 2 (37). P. 4–10. doi: http://doi.org/10.15587/2312-8372.2017.110868 

Miroshnichenko N. Mirovoy rynok AR dostignet obema v 198 milliardov dollarov k 2025 godu. BIS Research // Novosti VR industrii. 2018. URL: https://vrgeek.ru/mirovoj-rynok-ar-dostignet-obema-v-198-milliardov-dollarov-k-2025-godu/2018

Santos F. M., Silva V. F., Almeida L. M. A robust self-localization system for a small mobile autonomous robot // International Symphosium on Robotics and Automation. 2002. P. 1–6. URL: http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.134.2502

Antoni D., Ban Z., Zagar M. Demining Robots – Requirements and Constraints // Automatika. 2001. Vol. 42, Issue 3-4. P. 189–197.

Melo L. F. de, Rosário J. M., Junior A. F. da S. Mobile Robot Indoor Autonomous Navigation with Position Estimation Using RF Signal Triangulation // Positioning. 2013. Vol. 4, Issue 1. P. 20–35. doi: http://doi.org/10.4236/pos.2013.41004 

Zakharov A. A., Tuzhilkin A. Yu., Vedenin A. S. Algoritm opredeleniya polozheniya i orientatsii trekhmernykh obektov po videoizobrazheniyam na osnove veroyatnostnogo podkhoda // Fundamentalnye issledovaniya. 2014. Issue 11-8. P. 1683–1687.

Menache A. Understanding motion capture for computer animation. The Morgan Kaufmann Series In Computer Graphics, 2011. 254 p.

Tobon R. The Mocap Book: A Practical Guide to the Art of Motion Capture. Forisforce, 2010. 258 p.

Nguyen V., Harati A., Siegwart R. Lightweight SLAM algorithm using orthogonal planes for indoor mobile robotics // Intelligent Robots and Systems. 2007. P. 658–663. doi: http://doi.org/10.1109/iros.2007.4399512 

Yuldashev M. N. Ul'trazvukovye sistemy dlya opredeleniya prostranstvennogo polozheniya podvizhnogo obekta: Proceedings // Naukoemkie tekhnologii i intellektual'nye sistemy 2015. Moscow: MGTU im. N. E. Baumana, 2015. P. 465–472.

Methods and technologies of monitoring of the position of a mobile object in space: Proceedings / Nechyporenko O. V. et. al. // Kompiuterne modeliuvannia ta optymizatsiia skladnykh system (KMOSS-2018). Dnipro: Balans-klub, 2018. P. 193–195.

Aulinas J. The SLAM Problem: A Survey // Proceedings of the 2008 Conference on Artificial Intelligence Research & Development. 2008. P. 363–71. URL: http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.163.6439







Copyright (c) 2018 Olga Nechyporenko, Yaroslav Korpan

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ISSN (print) 2664-9969, ISSN (on-line) 2706-5448