OBJECT-ORIENTED SEMANTIC MAPPING AND DYNAMIC OPTIMIZATION ON A MOBILE ROBOT

Chi Guo,∗ Kai Huang,∗∗ Yarong Luo,∗ Huyin Zhang,∗∗ and Wenwei Zuo∗∗∗

References

  1. [1] N. Sünderhauf, et al., Place categorization and semantic mapping on a mobile robot, 2016 IEEE International Conference on Robotics and Automation (ICRA) (IEEE, Stockholm, Sweden, 2016).
  2. [2] N. Sünderhauf, et al., Meaningful maps with object-oriented semantic mapping, 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (IEEE, Vancouver, Canada, 2017).
  3. [3] J. McCormac, et al., Semanticfusion: Dense 3D semantic mapping with convolutional neural networks, 2017 IEEE International Conference on Robotics and Automation (ICRA) (IEEE, Singapore, Singapore, 2017).
  4. [4] W. Hess, et al., Real-time loop closure in 2D LIDAR SLAM, 2016 IEEE International Conference on Robotics and Automation (ICRA) (IEEE, Stockholm, Sweden, 2016).
  5. [5] R. Mur-Artal and J.D. Tards, ORB-SLAM2: An open-source SLAM system for monocular, stereo, and RGB-D cameras, IEEE Transactions on Robotics, 33(5), 2017, 1255–1262.
  6. [6] C. Forster, M. Pizzoli, and D. Scaramuzza, SVO: Fast semidirect monocular visual odometry, 2014 IEEE International Conference on Robotics and Automation (ICRA) (IEEE, Hong Kong, China, 2014).
  7. [7] J. Engel, T. Schöps, and D. Cremers, LSD-SLAM: Large-scale direct monocular SLAM, European Conference on Computer Vision (Cham: Springer, 2014).
  8. [8] J. Redmon and A. Farhadi, Yolov3: An incremental improvement, arXiv preprint arXiv:1804.02767, 2018.
  9. [9] S. Thrun, Robotic mapping: A survey, Exploring Artificial Intelligence in the New Millennium, 1(1), 2002, 1–35.
  10. [10] B. Zhou, et al., Learning deep features for scene recognition using places database, Advances in Neural Information Processing Systems, 2014, 487–495.
  11. [11] B. Zhou, et al., Places: A 10 million image database for scene recognition, IEEE Transactions on Pattern Analysis and Machine Intelligence, 40(6), 2017, 1452–1464.
  12. [12] S. Ren, et al., Faster R-CNN: Towards real-time object detection with region proposal networks, IEEE Transactions on Pattern Analysis and Machine Intelligence, 39(6), 2016, 1137–1149.
  13. [13] W. Liu, et al., SSD: Single shot multibox detector, European Conference on Computer Vision (Springer, Cham, 2016).
  14. [14] J. Long, E. Shelhamer, and T. Darrell, Fully convolutional networks for semantic segmentation, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, (Boston, America, 2015).
  15. [15] L.-C. Chen, et al., Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFS, IEEE Transactions on Pattern Analysis and Machine Intelligence, 40(4), 2017, 834–848.
  16. [16] P. Li, et al., Deep visual tracking: Review and experimental comparison, Pattern Recognition, 76, 2018, 323–338.
  17. [17] L. Liu, et al., Deep learning for generic object detection: A survey, International Journal of Computer Vision, 128(2), 2020, 261–318. 9
  18. [18] R.F. Salas-Moreno, et al., SLAM++: Simultaneous localisation and mapping at the level of objects, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, (Portland, America, 2013).
  19. [19] N. Sünderhauf, et al., Meaningful maps with object-oriented semantic mapping, 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (IEEE, Vancouver, Canada, 2017).
  20. [20] T. Whelan, et al., ElasticFusion: Real-time dense SLAM and light source estimation, The International Journal of Robotics Research, 35(14), 2016, 1697–1716.
  21. [21] S. Saeedi, et al., Occupancy grid map merging for multiple robot simultaneous localization and mapping, International Journal of Robotics and Automation, 30(2), 2015, 149–157.
  22. [22] H. Sun, Z. Meng, and M.H. Ang, Semantic mapping and semantics-boosted navigation with path creation on a mobile robot, 2017 IEEE International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM) (IEEE, Ningbo, China, 2017).
  23. [23] L.F. Posada, et al., Semantic mapping with omnidirectional vision, 2018 IEEE International Conference on Robotics and Automation (ICRA) (IEEE, Brisbane, Australia, 2018).
  24. [24] C. Chen and H. Wang, Large-scale loop-closing by fusing range data and aerial image, International Journal of Robotics and Automation, 22(2), 2007, 160–169.
  25. [25] B. Douillard, et al., Classification and semantic mapping of urban environments, The International Journal of Robotics Research, 30(1), 2011, 5–32.
  26. [26] J. Redmon, Darknet: Open source neural networks in C, 2013, http://pjreddie.com/darknet
  27. [27] T.-Y. Lin, et al., Microsoft COCO: Common objects in context, European Conference on Computer Vision (Springer, Cham, 2014).
  28. [28] M. Przybyla, Detection and tracking of 2D geometric obstacles from LRF data, 2017 11th International Workshop on Robot Motion and Control (RoMoCo) (IEEE, Wasowo, Poland, 2017).
  29. [29] Q. Zhang and R. Pless, Extrinsic calibration of a camera and laser range finder (improves camera calibration), 2004 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (IEEE Cat. No. 04CH37566), Vol. 3 (IEEE, Sendai, Japan, 2004).
  30. [30] S. Agarwal, K. Mierle, et al., Ceres solver, http://ceressolver.org
  31. [31] G. Grisetti, C. Stachniss, and W. Burgard, Improved techniques for grid mapping with rao-blackwellized particle filters, IEEE Transactions on Robotics, 23(1), 2007, 34–36.
  32. [32] M. Ester, et al., A density-based algorithm for discovering clusters in large spatial databases with noise, KDD, 96(34), 1996.
  33. [33] J. Dai, et al., R-FCN: Object detection via region-based fully convolutional networks, Advances in Neural Information Processing Systems, 2016, 379–387.
  34. [34] X. Chen, et al., Suma++: Efficient LiDAR-based semantic slam, 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (IEEE, Macau, Chian, 2019).
  35. [35] A. Bochkovskiy, C.-Y. Wang, and H.-Y.M. Liao, YOLOv4: Optimal speed and accuracy of object detection, arXiv preprint arXiv:2004.10934, 2020.
  36. [36] Z. Li, et al., Stability analysis of linear systems with timevarying delay via intermediate polynomial-based functions, Automatica, 113, 2020, 108756.
  37. [37] Z. Li, et al., Stability analysis for delayed neural networks via improved auxiliary polynomial-based functions, IEEE Transactions on Neural Networks and Learning Systems, 30(8), 2018, 2562–2568.
  38. [38] Y. Wang, W. Zhou, J. Luo, et al., Reliable intelligent path following control for a robotic airship against sensor faults, IEEE/ASME Transactions on Mechatronics, 24(6), 2020, 2572–2582.

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