Диссертация (1090939), страница 18
Текст из файла (страница 18)
A comparison of EKF, UKF, FastSLAM2.0,and UKF-based FastSLAM algorithms // Intelligent Engineering Systems (INES),2012 IEEE 16th International Conference on, Lisbon, 2012, pp. 37–43.54.Lee T., Hollerer T. Hybrid Feature Tracking and User Interaction forMarkerless Augmented Reality // Virtual Reality Conference, 2008.
VR '08. IEEE,Reno, NE, 2008, pp. 145–152.55.Leonard, J.J., Durrant-whyte, H.F. Simultaneous map building andlocalization for an autonomous mobile robot // Intelligent Robots and Systems'91.'Intelligence for Mechanical Systems, Proceedings IROS'91. IEEE/RSJInternational Workshop on: 1442–1447.56.Lowe D. Distinctive image features from scale-invariant keypoints //IJCV 60, 2004.57.Macknojia R., Chavez-Aragon A., Payeur P., Laganiere R. Calibrationof a Network of Kinect Sensors for Robotic Inspection over a Large Workspace //Proceedings of the 2013 IEEE Workshop on Robot Vision (WORV), Clearwater,FL, USA, 15–17 January 2013; pp. 184–190.58.Mikolajczyk K., Leibe B., Schiele B.
Multiple object class detectionwith a generative model // Proc of CVPR 2006, pp. 26–36.59.Mikolajczyk K., Schmid C. Scale and affine invariant interest pointdetectors // International Journal of Computer Vision, 2004.60.Miksik O., Mikolajczyk K. Evaluation of Local Detectors andDescriptors for Fast Feature Matching // Proceedings of the InternationalConference on Pattern Recognition (ICPR), Tsukuba Science City, Japan, 201261.Moravec H. Obstacle Avoidance and Navigation in the Real Worldby a Seeing Robot Rover // Tech Report CMU-RI-TR-3 Carnegie-MellonUniversity, 1980.13862.Murphy K. Bayesian Map Learning in Dynamic Environments //NIPS '99 (Neural Info.
Proc. Systems).63.forMurphydynamicK.,RussellbayesianS.networksRao-blackwellized//SequentialparticleMonteCarlofilteringMethodsin Practice, Springer, 2001.64.Neubeck A., Van Gool L.Efficient Non-Maximum Supression //International Conference on Pattern Recognition, 2006.65.Oxford Dataset // http://robots.ox.ac.uk/~vgg/data/data-aff.html.66.Pajdla T.Elements of Geometry for Computer Vision //https://cw.felk.cvut.cz/courses/GVG/2013/Lecture/GVG- 2013-Lecture.pdf, 2014.67.Prozorov A., Priorov A.
Three-Dimensional Reconstruction of a Scenewith the Use of Monocular Vision // Measurement Techniques, Vol. 57, N. 10,January 2015.68.Prozorov A., Priorov A. Methods of complete surface reconstructionthrough merging of point clouds according to stereo vision data // OpenInnovations Association FRUCT, Proceedings of 16th Conference, IEEE, p. 73–78, December 2014.69.Prozorov A., Volokhov V., Priorov A. Monocular visual odometry and3D reconstruction // Proceedings of the15th Conference of Open InnovationsAssociation, FRUCT 2014; Saint-Petersburg; Russian Federation. pp.
112–118.70.Prozorov, A.; Tyukin, A.; Lebedev, I.; Priorov, A. Self-localization ofmobile robot in unknown environment // in Open Innovations Association(FRUCT), 2015 17TH Conference of , vol., no., pp.173-178, 20–24, April 2015.71.PUT Kinect 1 & Kinect 2 data set // http://lrm.put.poznan.pl/putkk/.72.Quinlan J.R. Induction of decision trees // Machine Learning, 1986.73.Raposo C., Barreto J.P., Nunes U. Fast and Accurate Calibration of aKinect Sensor // International Conference on 3DV, 2013.74.RGB-DSLAMDatasethttps://vision.in.tum.de/data/datasets/rgbd-dataset.139andBenchmark//75.Rodehorst V., Koschan A. Comparison and evaluation of feature pointdetectors.
2006.76.Rolo D. Graph-based 6D SLAM for RGB-D Sensors and HybridVision and Depth Camera Localization // Instituto Superior Tecnico, Universidadede Lisboa, 2013.77.Rosten E., Drummond T. Machine Learning for high-speed cornerdetection // 9th European Conference on Computer Vision (ECCV 2006), 2006.78.Rublee E., Rabaud V., Konolige K., Bradski G. ORB: an efficientalternative to SIFT or SURF // Computer Vision (ICCV), 2011 IEEE InternationalConference on. IEEE, 2011.79.Shehu V., Dika A. Object class recognition using range of multiplecomputer vision algorithms // MIPRO, 2011 Proceedings of the 34th InternationalConvention, Opatija, 2011, pp. 909–912.80.Simon J., Jeffrey U. Unscented Filtering and Nonlinear Estimation //Proc.
IEEE Vol. 92, No. 3 (March 2004), p. 401–22.81.Smisek J., Jancosek M., Pajdla T. 3D with Kinect // IEEE Workshopon Consumer Depth Cameras for Computer Vision, 2011.82.Sonka M., Hlavac V., Boyle R. Image Processing, Analysis andMachine Vision // Thomson, 2008.83.Sturm J., Engelhard N., Endres F., Burgard W., Cremers D. Abenchmark for the evaluation of RGB-D SLAM systems // Intelligent Robots andSystems (IROS), 2012 IEEE/RSJ International Conference on, Vilamoura, 2012,pp. 573–580.84.Szeliski R.Computer Vision: Algorithms and Applications //Springler, 2010.85.Taylor S., Rosten E., Drummond T. Robust Feature Matching //Workshop, IEEE Conference on Computer Vision and Pattern Recognition, 2009,pp. 15–22.86.TheMalagaStereoandhttp://www.mrpt.org/MalagaUrbanDataset.140LaserUrbanDataSet//87.Thrun S., Montemerlo M.
The GraphSLAM Algorithm WithApplications to Large-Scale Mapping of Urban Structures // International Journalon Robotics Research, 2005, Volume 25, 403–430.88.Tomasi C., Kanade T. Detection and tracking of point features // TRCarnegie-Melon University, 1992.89.Tuytelaars T., Mikolajczyk K. Local Invariant Feature Detectors: ASurvey // Foundations and Trends in Computer Graphics and Vision, 2007.90.Viola P., Jones M.J.Robust Real-Time Face Detection //International Journal of Computer Vision 57(2)., 2004.91.Wagner D., Mulloni A., Langlotz T., Schmalstieg D.
RealtimePanoramic Mapping and Tracking on Mobile Phones // Proceedings of the IEEEVirtual Reality Conference, 201092.Wagner D., Schmalstieg D., Bischof, H. Multiple Target Detection andTracking with Guaranteed Framerates on Mobile Phones // Proceedings of the 8thIEEE intl. Symposium on Mixed and Augmented Reality, 2009, pp. 57–64.93.Wan E.A., Van Der Merwe R. The unscented Kalman filter fornonlinear estimation // Adaptive Systems for Signal Processing, Communications,and Control Symposium 2000.
AS-SPCC. The IEEE 2000, Lake Louise, Alta.,2000, pp. 153–158.94.Wan E. A., Van der Merwe R. The square-root unscented Kalmanfilter for state and parameter-estimation // Acoustics, Speech, and SignalProcessing, 2001. Proceedings. (ICASSP '01). 2001 IEEE International Conferenceon, Salt Lake City, UT, 2001, pp. 3461–3464.95.Willems G., Tuytelaars T., Van Gool L.
An efficient dense and scale-invariant spatio-temporal interest point detector // ECCV, 2008.96.Yilmaz O., Karakus F. Stereo and kinect fusion for continuous 3Dreconstruction and visual odometry // Electronics, Computer and Computation(ICECCO), 2013 International Conference on, Ankara, 2013, pp. 115–118.97.Yuan Z., Rosa S., Russo L.O., Bona B.
A Kinect-based Front-end forGraph-SLAM Using Plane Matching in Planar Indoor Environment // Intelligent141Autonomous Systems 13, Volume 302 of the series Advances in IntelligentSystems and Computing pp 389–400.98.Zakaria A. M., Said A.
M. 3D Reconstruction of a scene from multipleuncalibrated images using close range photogrammetry // Information Technology(ITSim), 2010 International Symposium in, Kuala Lumpur, 2010, pp. 1–5.99.Zhang W., Liu M, Zhao Z. Accuracy Analysis of UnscentedTransformation of Several Sampling Strategies // Proc. of the 10th Intl. Conf.
onSoftware Engineering, Artificial Intelligence, Networking and Parallel/DistributedComputing. ACIS, 2009.100. Zhang L, Liu Z, Jiao J. An improved RANSAC algorithm usingwithin-class scatter matrix for fast image stitching. In: Astola JT, Egiazarian KO,editors // Image Processing: Algorithms and Systems IX; January 2011; SanFrancisco, Calif, USA.142ПРИЛОЖЕНИЕ 1. СВИДЕЛЬСТВА О РЕГИСТРАЦИИПРОГРАММНОГО ОБЕСПЕЧЕНИЯ143144ПРИЛОЖЕНИЕ 2.
АКТЫ ВНЕДРЕНИЯ РЕЗУЛЬТАТОВ РАБОТЫ145146147148.
![](https://s.studizba.com/z.php?f=/templates/si/i/noavatar.png&w=100&h=100&t=1"){kind=avatar}
