APPLICATION OF NEURAL NETWORKS FOR ROBOT 3D MAPPING AND ANNOTATION USING DEPTH IMAGE CAMERA, 529-536.

Tran D. Dung and Genci Capi

References

  1. [1] G.A. Zachiotis, G. Andrikopoulos, R. Gornez, K. Nakamura,et al., A survey on the application trends of home servicerobotics, 2018 IEEE International Conference on Roboticsand Biomimetics (ROBIO), Kuala Lumpur, Malaysia, 2018,1999–2006.
  2. [2] J.J. Leonard and H.F. Durrant-Whyte, Simultaneous mapbuilding and localization for an autonomous mobile robot,Proceedings IROS’91: IEEE/RSJ International Workshop onIntelligent Robots and Systems’ 91, Osaka, Japan, 1991, 1442–1447.
  3. [3] T. Taketomi, U. Hideaki, and S. Ikeda, Visual SLAM algo-rithms: A survey from 2010 to 2016, IPSJ Transactions onComputer Vision and Applications, 9, 2017, 16.
  4. [4] A.J. Davison, I.D. Reid, N.D. Molton, and O. Stasse,MonoSLAM: Real-time single camera SLAM, IEEE Transac-tions on Pattern Analysis & Machine Intelligence, 6, 2007,1052–1067.
  5. [5] G. Klein and D. Murray, Parallel tracking and mapping for smallAR workspaces, Proceedings of the IEEE ACM InternationalSymposium Mixed Augmented Reality, Nara, Japan, November2007, 225–234.
  6. [6] R. Mur-Artal, J.M.M. Montiel, and J.D. Tardos, ORB-SLAM:A versatile and accurate monocular SLAM system, IEEETransactions on Robotics, 31(5), 2015, 1147–1163.
  7. [7] R.A. Newcombe, S.J. Lovegrove, and A.J. Davison, DTAM:Dense tracking and mapping in real-time, IEEE InternationalConference on Computer Vision, Barcelona, Spain, 2011,2320–2327.
  8. [8] J. Engel, T. Sch¨ops, and D. Cremers, LSD-SLAM: Large-scaledirect monocular SLAM, European Conference on ComputerVision, Zurich, Switzerland, 2014, 834–849.
  9. [9] C. Forste, M. Pizzoli, and D. Scaramuzza, SVO: Fast semi-directmonocular visual odometry, IEEE International Conference onRobotics and Automation (ICRA), Hong Kong, China, 2014,15–22.
  10. [10] J. Engel, V. Koltun, and D. Cremers, Direct sparse odom-etry, IEEE Transactions on Pattern Analysis and MachineIntelligence, 40(3), 2017, 611–625.
  11. [11] R.A. Newcombe, S. Izadi, O. Hilliges, D. Molyneaux, et al.,Kinectfusion: Real-time dense surface mapping and tracking,10th IEEE International Symposium on Mixed and AugmentedReality ISMAR, Basel, Switzerland, 2011, 127–136.
  12. [12] R.F. Salas-Moreno, R.A. Newcombe, H. Strasdat, P.H. Kelly,et al., Slam++: Simultaneous localisation and mapping atthe level of objects, Proceedings of the IEEE Conference onComputer Vision and Pattern Recognition, Orlando, USA,2013, 1352–1359.
  13. [13] T.D. Dung, D. Hossain, S.I. Kaneko, and G. Capi, Multi-feature image indexing for robot localization in texturelessenvironments, Robotics, 8(2), 2019, 37.
  14. [14] P.J. Herrera, G. Pajares, M. Guijarro, J.J. Ruz, et al., Afeatured-based strategy for stereovision matching in sensorswith fish-eye lenses for forest environments, Sensors, 9(12),2009, 9468–9492.
  15. [15] J. McCormac, A. Handa, A. Davison, S. Leutenegger,et al., Semanticfusion: Dense 3D semantic mapping withconvolutional neural networks, IEEE International Confer-ence on Robotics and automation (ICRA), Singapore, 2017,4628–4635.
  16. [16] C. Zhao, L. Sun, P. Purkait, T. Duckett, et al., Dense RGB-Dsemantic mapping with pixel-voxel neural network, Sensors,18(9), 2018, 3099.
  17. [17] T. Whelan, S. Leutenegger, R. Salas-Moreno, B. Glocker,et al., In: Proceedings of Robotics: Science and Systems.ElasticFusion: Dense SLAM without a pose graph, Robotics:Science and Systems, 2015.
  18. [18] J.W. Hart, R. Shah, S. Kirmani, N. Walker, et al., PRISM:Pose registration for integrated semantic mapping, IEEE/RSJInternational Conference on Intelligent Robots and Systems(IROS), Madrid, Spain, 2018, 896–902.
  19. [19] C. Case, B. Suresh, A. Coates, and A.Y. Ng, Autonomous signreading for semantic mapping, IEEE International Conferenceon Robotics and Automation, Shanghai, China, 2011, 3297–3303.
  20. [20] M. Mielle, M. Magnusson, and A.J. Lilienthal, A methodto segment maps from different modalities using free spacelayout MAORIS: Map of ripples segmentation, InternationalConference on Robotics and Automation (ICRA), Brisbane,Australia, 2018, 4993–4999.
  21. [21] R. Bormann, F. Jordan, W. Li, J. Hampp et al., Roomsegmentation: Survey, implementation, and analysis, IEEEInternational Conference on Robotics and Automation (ICRA),Stockholm, Sweden, 2016, 1019–1026.
  22. [22] Y. He, B. Liang, Y. Zou, and J. Yang, Depth errors analysisand correction for time-of-flight (ToF) cameras, Sensors, 17(1),2017, 92.
  23. [23] L.E. Kavraki, P. Svestka, J.C. Latombe, and M.H. Overmars,Probabilistic roadmaps for path planning in high-dimensionalconfiguration spaces, IEEE Transactions on Robotics andAutomation, 12(4), 1996, 566–580.
  24. [24] R.C. Coulter, implementation of the pure pursuit path trackingalgorithm, CMU-RI-TR-92-01. Carnegie-Mellon UNIV Pitts-burgh PA Robotics INST, 1992.
  25. [25] T.D. Dung, H. Delowar, and G. Capi, Neural network-basedrobot navigation in indoor environments using depth image,The IEEJ International Workshop on Sensing, Actuation,Motion Control, and Optimization (SAMCON), Chiba, Japan,2019, 1–6.
  26. [26] X. Li and R. Belaroussi, Semi-dense 3D semantic map-ping from monocular slam, arXiv preprint arXiv:1611.04144,2016.
  27. [27] S. Yang, Y. Huang, and S. Scherer, Semantic3D occupancy mapping through efficient high orderCRFs, IEEE/RSJ International Conference on IntelligentRobots and Systems (IROS), Vancouver, Canada, 2017,590–597.

Important Links:

Go Back