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On the Representation of Planes for Efficient Graph-based SLAM with High-level Features

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Języki publikacji
EN
Abstrakty
EN
Despite the fact, that dense SLAM systems are extensively developed and are getting popular, feature based ones still have many advantages over them. One of the most important matters in sparse systems are features. The performance and robustness of a system depends strictly on the quality of constraints imposed by feature observations and reliable matching between measurements and features. To improve those two aspects, higher-level features can be used, and planes are a natural choice. We tackle the problem of plugging planes into the g2o optimization framework with two distinct plane representations: one based on a properly stated SE(3) parametrization and one based on a minimal parametrization analogous to quaternions. Proposed solutions were implemented as extensions to the g2o framework and experiments that verify them were conducted using simulation. We provide a comparison of performance under various conditions that emphasized differences.
Twórcy
  • Poznań University of Technology, Institute of Control and Information Engineering, ul. Piotrowo 3A, 60-965 Poznań, Poland
Bibliografia
  • [1] H. Bay, A. Ess, T. Tuytelaars, and L. Van Gool, “Speeded-Up Robust Features (SURF)”, Comput. Vis. Image Underst., vol. 110, no. 3, 2008, 346–359.
  • [2] D. Belter, M. Nowicki, and P. Skrzypczyński. “Accurate Map-Based RGB-D SLAM for Mobile Robots”. In: L. P. Reis, A. P. Moreira, P. U. Lima, L. Montano, and V. Muñoz Martinez, eds., Robot 2015: Second Iberian Robotics Conference, volume 418 of Advances in Intelligent and Soft Computing (AISC), 533–545. Springer International Publishing, 2016.
  • [3] D. Belter, M. Nowicki, and P. Skrzypczyński, “Improving accuracy of feature-based RGB-D SLAM by modeling spatial uncertainty of point features”. In: 2016 IEEE International Conference on Robotics and Automation (ICRA), 2016, 1279–1284.
  • [4] J. A. Castellanos, J. M. M. Montiel, J. Neira, and J. D. Tardos, “The SPmap: a probabilistic framework for simultaneous localization and map building”, IEEE Transactions on Robotics and Automation, vol. 15, no. 5, 1999, 948–952.
  • [5] J. Engel, J. Stückler, and D. Cremers, “Large-scale direct SLAM with stereo cameras”. In: Intelligent Robots and Systems (IROS), 2015 IEEE/RSJ International Conference on, 2015, 1935–1942.
  • [6] G. Grisetti, R. Kümmerle, and K. Ni, “Robust optimization of factor graphs by using condensed measurements”. In: 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2012, 581–588.
  • [7] A. Handa, T. Whelan, J. McDonald, and A. J. Davison, “A benchmark for rgb-d visual odometry, 3d reconstruction and slam”. In: 2014 IEEE International Conference on Robotics and Automation (ICRA), 2014, 1524–1531.
  • [8] M. Kaess, “Simultaneous Localization and Mapping with Infiinite Planes”. In: IEEE Intl. Conf.on Robotics and Automation, ICRA, Seattle, WA,2015, 4605 – 4611.
  • [9] M. Kaess, A. Ranganathan, and F. Dellaert, “iSAM: Incremental Smoothing and Mapping”, IEEE Transactions on Robotics, vol. 24, no. 6, 2008, 1365–1378.
  • [10] C. Kerl, J. Sturm, and D. Cremers, “Dense visual SLAM for RGB-D cameras”. In: 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2013, 2100–2106.
  • [11] R. Kümmerle, G. Grisetti, H. Strasdat, K. Konolige, and W. Burgard, “G2o: A general framework for graph optimization”. In: Robotics and Automation (ICRA), 2011 IEEE International Conference on, 2011, 3607–3613.
  • [12] R. Mur-Artal, J. M. M. Montiel, and J. D. Tardós, “ORB-SLAM: A Versatile and Accurate Monocular SLAM System”, IEEE Transactions on Robotics, vol. 31, no. 5, 2015, 1147–1163.
  • [13] R. A. Newcombe, S. Izadi, O. Hilliges, D. Molyneaux, D. Kim, A. J. Davison, P. Kohi, J. Shotton, S. Hodges, and A. Fitzgibbon, “Kinect- Fusion: Real-time dense surface mapping and tracking”. In: Mixed and Augmented Reality (ISMAR), 2011 10th IEEE International Symposium on, 2011, 127–136.
  • [14] M. Nowicki and P. Skrzypczyński, “Experimental Verifiication of a Walking Robot Self-Localization System with the Kinect Sensor”, Journal of Automation, Mobile Robotics and Intelligent Systems, vol. 7, no. 4, 2013, 42–52.
  • [15] E. Rublee, V. Rabaud, K. Konolige, and G. Bradski, “ORB: An ef􀏐icient alternative to SIFT or SURF”. In: 2011 International Conference on Computer Vision, 2011, 2564–2571.
  • [16] R. F. Salas-Moreno, B. Glocken, P. H. J. Kelly, and A. J. Davison, “Dense planar SLAM”. In: Mixed and Augmented Reality (ISMAR), 2014 IEEE International Symposium on, 2014, 157–164.
  • [17] J. Sturm, N. Engelhard, F. Endres, W. Burgard, and D. Cremers, “A Benchmark for the Evaluation of RGB-D SLAM Systems”. In: Proc. of the International Conference on Intelligent Robot Systems (IROS), 2012.
  • [18] Y. Taguchi, Y. D. Jian, S. Ramalingam, and C. Feng, “Point-plane SLAM for hand-held 3D sensors”. In: Robotics and Automation (ICRA), 2013 IEEE International Conference on, 2013, 5182–5189.
  • [19] J. Weingarten and R. Siegwart, “3D SLAM using planar segments”. In: 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2006, 3062–3067.
Uwagi
PL
Opracowanie ze środków MNiSW w ramach umowy 812/P-DUN/2016 na działalność upowszechniającą naukę.
Typ dokumentu
Bibliografia
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