| DC Field | Value | Language |
|---|
| dc.contributor.author | Moskalenko, Vyacheslav | |
| dc.contributor.author | Moskalenko, Alona | |
| dc.contributor.author | Korobov, Artem | |
| dc.contributor.author | Boiko, Olha | |
| dc.contributor.author | Martynenko, Serhii | |
| dc.contributor.author | Borovenskyi, Oleksandr | |
| dc.coverage.temporal | 21-25 August 2018, Lviv | |
| dc.date.accessioned | 2020-06-19T12:04:27Z | - |
| dc.date.available | 2020-06-19T12:04:27Z | - |
| dc.date.created | 2018-02-28 | |
| dc.date.issued | 2018-02-28 | |
| dc.identifier.citation | Model and Training Methods of Autonomous Navigation System for Compact Drones / Vyacheslav Moskalenko, Alona Moskalenko, Artem Korobov, Olha Boiko, Serhii Martynenko, Oleksandr Borovenskyi // Data stream mining and processing : proceedings of the IEEE second international conference, 21-25 August 2018, Lviv. — Львів : Lviv Politechnic Publishing House, 2018. — P. 503–508. — (Machine Vision and Pattern Recognition). | |
| dc.identifier.isbn | © Національний університет „Львівська політехніка“, 2018 | |
| dc.identifier.isbn | © Національний університет „Львівська політехніка“, 2018 | |
| dc.identifier.uri | https://ena.lpnu.ua/handle/ntb/52433 | - |
| dc.description.abstract | The paper presents a novel model of
convolutional neural network for visual feature extraction,
support vector machine for position prediction and
information-extreme classifier for obstacle prediction with new
training methods to build decision rules of autonomous
navigation system for compact drones are presented in the
paper. Sparse-coding neural gas algorithm for unsupervised
training of the convolution filters, supervised incremental
learning method for training the regression model and particle
swarm optimization algorithm for training the classifier model
are proposed. The complex criterion for choosing parameter of
feature extractor model is considered. Simulation results with
optimal model on test open datasets confirm the suitability of
proposed algorithms for practical usage. | |
| dc.format.extent | 503-508 | |
| dc.language.iso | en | |
| dc.publisher | Lviv Politechnic Publishing House | |
| dc.relation.ispartof | Data stream mining and processing : proceedings of the IEEE second international conference, 2018 | |
| dc.relation.uri | http://cs231n.stanford.edu/reports/ | |
| dc.relation.uri | https://arxiv.org/pdf/1611.06069.pdf | |
| dc.relation.uri | https://www.researchgate.net/publication/238519783_A_New_SVR_I | |
| dc.subject | navigation | |
| dc.subject | visual odometry | |
| dc.subject | convolutional neural network | |
| dc.subject | neural gas | |
| dc.subject | information criterion | |
| dc.subject | support vector regression | |
| dc.title | Model and Training Methods of Autonomous Navigation System for Compact Drones | |
| dc.type | Conference Abstract | |
| dc.rights.holder | © Національний університет “Львівська політехніка”, 2018 | |
| dc.contributor.affiliation | Sumy State University | |
| dc.format.pages | 6 | |
| dc.identifier.citationen | Model and Training Methods of Autonomous Navigation System for Compact Drones / Vyacheslav Moskalenko, Alona Moskalenko, Artem Korobov, Olha Boiko, Serhii Martynenko, Oleksandr Borovenskyi // Data stream mining and processing : proceedings of the IEEE second international conference, 21-25 August 2018, Lviv. — Lviv Politechnic Publishing House, 2018. — P. 503–508. — (Machine Vision and Pattern Recognition). | |
| dc.relation.references | [1] S. Wang, Z. Deng, and G. Yin, An Accurate GPS-IMU/DR Data Fusion Method for Driverless Car Based on a Set of Predictive Models and Grid Constraints,’ Basel, Switzerland : Multidisciplinary Digital Publishing Institute, vol. 16(3), pp. 280–293, 2016. | |
| dc.relation.references | [2] B. Suwandi, T. Kitasuka, and M. Aritsugi, “Low-cost IMU and GPS fusion strategy for apron vehicle positioning,” TENCON 2017, IEEE Region 10 Conference, Penang, Malaysia, pp. 449–454, Nov. 2017. | |
| dc.relation.references | [3] B. A. Mary, and P. H. Gerhard, “Pose Estimation of a Mobile Robot Based on Fusion of IMU Data and Vision Data Using an Extended Kalman Filter,” Basel, Switzerland : Multidisciplinary Digital Publishing Institute, vol. 17(10), pp. 2164, 2017. | |
| dc.relation.references | [4] J. Folkesson, J. Leederkerken, R. Williams, and A. Patrikalakis, “A Feature Based Navigation System for an Autonomous Underwater Robot,” In: Laugier C., Siegwart R. (eds) Field and Service Robotics. Springer Tracts in Advanced Robotics. Springer : Berlin/Heidelberg, 2008. vol. 42, pp. 105-114. | |
| dc.relation.references | [5] C. Cadena, L. Carlone, H. Carrillo, Y. Latif, D. Scaramuzza, J. Neira, I. Reid, and J. J. Leonard, “Past, Present, and Future of Simultaneous Localization And Mapping: Towards the Robust-Perception Age,” IEEE Transactions on Robotics, vol. 32, no. 6, pp. 1309–1332, 2016. | |
| dc.relation.references | [6] G.-L. Dorian, S. Marta, D. T. Juan, and J.M.M. Montiel, “Real-time monocular object SLAM. Robotics and Autonomous Systems,” North-Holland Publishing Co. : Amsterdam, Netherlands, 2016. vol.75, i. PB, pp. 435–449. | |
| dc.relation.references | [7] Th. Ayoul, T. Buckley, and F. Crevier. UAV Navigation above Roads Using Convolutional Neural Networks Available from: http://cs231n.stanford.edu/reports/ 2017/pdfs/553.pdf. | |
| dc.relation.references | [8] V. Mohanty DeepVO: A Deep Learning approach for Monocular Visual Odometry. Computer Vision and Pattern Recognition, 2016. Available from: https://arxiv.org/pdf/1611.06069.pdf | |
| dc.relation.references | [9] K. Labusch, E. Barth, and T. Martinetz, “Sparse Coding Neural Gas: Learning of Overcomplete Data Representations,” Neurocomputing, Elsevier Science Publishers B. V.: Amsterdam, Netherlands, vol. 72, is. 7–9, pp. 1547–1555, 2009. | |
| dc.relation.references | [10] H. Xu, R. Wang, and K. Wang. A New SVR Incremental Algorithm Based on Boundary Vector. 2010. Available from: https://www.researchgate.net/publication/238519783_A_New_SVR_I ncremental_Algorithm_Based_on_Boundary_Vector | |
| dc.relation.references | [11] V. V. Moskalenko, and A. G. Korobov, “Information-extreme algorithm of the system for recognition of objects on the terrain with optimization parameter feature extraction,” Radio Electronics, Computer Science, Control. Zaporizhzhya National Technical University : Zaporizhzhya, Ukraine, no. 2, pp. 38–45, 2017. | |
| dc.relation.referencesen | [1] S. Wang, Z. Deng, and G. Yin, An Accurate GPS-IMU/DR Data Fusion Method for Driverless Car Based on a Set of Predictive Models and Grid Constraints,’ Basel, Switzerland : Multidisciplinary Digital Publishing Institute, vol. 16(3), pp. 280–293, 2016. | |
| dc.relation.referencesen | [2] B. Suwandi, T. Kitasuka, and M. Aritsugi, "Low-cost IMU and GPS fusion strategy for apron vehicle positioning," TENCON 2017, IEEE Region 10 Conference, Penang, Malaysia, pp. 449–454, Nov. 2017. | |
| dc.relation.referencesen | [3] B. A. Mary, and P. H. Gerhard, "Pose Estimation of a Mobile Robot Based on Fusion of IMU Data and Vision Data Using an Extended Kalman Filter," Basel, Switzerland : Multidisciplinary Digital Publishing Institute, vol. 17(10), pp. 2164, 2017. | |
| dc.relation.referencesen | [4] J. Folkesson, J. Leederkerken, R. Williams, and A. Patrikalakis, "A Feature Based Navigation System for an Autonomous Underwater Robot," In: Laugier C., Siegwart R. (eds) Field and Service Robotics. Springer Tracts in Advanced Robotics. Springer : Berlin/Heidelberg, 2008. vol. 42, pp. 105-114. | |
| dc.relation.referencesen | [5] C. Cadena, L. Carlone, H. Carrillo, Y. Latif, D. Scaramuzza, J. Neira, I. Reid, and J. J. Leonard, "Past, Present, and Future of Simultaneous Localization And Mapping: Towards the Robust-Perception Age," IEEE Transactions on Robotics, vol. 32, no. 6, pp. 1309–1332, 2016. | |
| dc.relation.referencesen | [6] G.-L. Dorian, S. Marta, D. T. Juan, and J.M.M. Montiel, "Real-time monocular object SLAM. Robotics and Autonomous Systems," North-Holland Publishing Co. : Amsterdam, Netherlands, 2016. vol.75, i. PB, pp. 435–449. | |
| dc.relation.referencesen | [7] Th. Ayoul, T. Buckley, and F. Crevier. UAV Navigation above Roads Using Convolutional Neural Networks Available from: http://cs231n.stanford.edu/reports/ 2017/pdfs/553.pdf. | |
| dc.relation.referencesen | [8] V. Mohanty DeepVO: A Deep Learning approach for Monocular Visual Odometry. Computer Vision and Pattern Recognition, 2016. Available from: https://arxiv.org/pdf/1611.06069.pdf | |
| dc.relation.referencesen | [9] K. Labusch, E. Barth, and T. Martinetz, "Sparse Coding Neural Gas: Learning of Overcomplete Data Representations," Neurocomputing, Elsevier Science Publishers B. V., Amsterdam, Netherlands, vol. 72, is. 7–9, pp. 1547–1555, 2009. | |
| dc.relation.referencesen | [10] H. Xu, R. Wang, and K. Wang. A New SVR Incremental Algorithm Based on Boundary Vector. 2010. Available from: https://www.researchgate.net/publication/238519783_A_New_SVR_I ncremental_Algorithm_Based_on_Boundary_Vector | |
| dc.relation.referencesen | [11] V. V. Moskalenko, and A. G. Korobov, "Information-extreme algorithm of the system for recognition of objects on the terrain with optimization parameter feature extraction," Radio Electronics, Computer Science, Control. Zaporizhzhya National Technical University : Zaporizhzhya, Ukraine, no. 2, pp. 38–45, 2017. | |
| dc.citation.conference | IEEE second international conference "Data stream mining and processing" | |
| dc.citation.spage | 503 | |
| dc.citation.epage | 508 | |
| dc.coverage.placename | Львів | |
| Appears in Collections: | Data stream mining and processing : proceedings of the IEEE second international conference
|
