Journal of East China Normal University(Natural Science) >

2019 , Vol. 2019 >Issue 6: 103 - 114

DOI: https://doi.org/10.3969/j.issn.1000-5641.2019.06.010

Computer Science

A plane-based localization scheme using RGB-D sensor for the Manhattan World assumption

  • JIANG Yu-hao ,
  • CHEN Lei
Expand
  • Department of Computer Science and Technology, East China Normal University, Shanghai 200062, China

Received date: 2019-01-11

  Online published: 2019-11-26

Fold

Abstract

Using the Manhattan World assumption with plane-based indoor localization, an indoor positioning scheme based on plane features in RGB-D vision is proposed, which can be used to extend SLAM (Simultaneous Localization and Mapping) systems. A matching process based on the main direction of the Manhattan Frame is designed to reduce the exponentially increasing time consumed. Simplified localization with 4 degrees of freedom is adopted after the initial pose determination for the problem of low efficiency during exploration. Small subgraphs in each frame are merged into one subgraph for matching to reduce the time consumed for repetitive subgraph matching. The proposed scheme not only effectively increases the success rate of scene matching, but also simplifies positioning in unknown scenes and improves positioning efficiency. Experimental results show that the method can achieve successful localization with shorter path lengths and reduce computational cost for real-time applications.

Key words： localization; SLAM (Simultaneous Localization and Mapping); Manhattan World assumption

Cite this article

JIANG Yu-hao , CHEN Lei . A plane-based localization scheme using RGB-D sensor for the Manhattan World assumption[J]. Journal of East China Normal University(Natural Science), 2019 , 2019(6) : 103 -114 . DOI: 10.3969/j.issn.1000-5641.2019.06.010

References

[1] GLOCKER B, SHOTTON J, CRIMINISI A, et al. Real-time RGB-D camera relocalization via randomized ferns for keyframe encoding[J]. IEEE transactions on visualization and computer graphics, 2015, 21(5):571-583.
[2] SE S, LOWE D G, LITTLE J J. Vision-based global localization and mapping for mobile robots[J]. IEEE Transactions on robotics, 2005, 21(3):364-375.
[3] MUR-ARTAL R, TARDÓS J D. Orb-slam2:An open-source slam system for monocular, stereo, and rgb-d cameras[J]. IEEE Transactions on Robotics, 2017, 33(5):1255-1262.
[4] SALAS-MORENO R F, NEWCOMBE R A, STRASDAT H, et al. Slam++:Simultaneous localisation and mapping at the level of objects[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. 2013:1352-1359.
[5] SHI Y, XU K, NIEßNER M, et al. Planematch:Patch coplanarity prediction for robust rgb-d reconstruction[C]//Proceedings of the European Conference on Computer Vision (ECCV). 2018:750-766.
[6] FORSTNER W, KHOSHELHAM K. Efficient and accurate registration of point clouds with plane to plane correspondences[C]//Proceedings of the IEEE International Conference on Computer Vision. 2017:2165-2173.
[7] TAGUCHI Y, JIAN Y D, RAMALINGAM S, et al. Point-plane SLAM for hand-held 3D sensors[C]//2013 IEEE International Conference on Robotics and Automation. IEEE, 2013:5182-5189.
[8] FERNÁNDEZ-MORAL E, RIVES P, ARÉVALO V, et al. Scene structure registration for localization and mapping[J]. Robotics and Autonomous Systems, 2016, 75:649-660.
[9] SALAS-MORENO R F, GLOCKEN B, KELLY P H J, et al. Dense planar SLAM[C]//2014 IEEE International Symposium on Mixed and Augmented Reality (ISMAR). IEEE, 2014:157-164.
[10] HSIAO M, WESTMAN E, ZHANG G, ET AL. Keyframe-based dense planar SLAM[C]//2017 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2017:5110-5117.
[11] MA L, KERL C, STÜCKLER J, et al. CPA-SLAM:Consistent plane-model alignment for direct RGB-D SLAM[C]//2016 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2016:1285-1291.
[12] CHO H G, YEON S, CHOI H, et al. Detection and compensation of degeneracy cases for IMU-kinect integrated continuous SLAM with plane features[J]. Sensors, 2018, 18(4):935(9pages).
[13] COUGHLAN J M, YUILLE A L. Manhattan world:Compass direction from a single image by bayesian inference[C]//Proceedings of the Seventh IEEE International Conference on Computer Vision. IEEE, 1999, 2:941-947.
[14] SCHINDLER G, DELLAERT F. Atlanta world:An expectation maximization framework for simultaneous lowlevel edge grouping and camera calibration in complex man-made environments[C]//Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'04). IEEE, 2004, 1:I-I.
[15] STRAUB J, ROSMAN G, FREIFELD O, et al. A mixture of manhattan frames:Beyond the manhattan world[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2014:3770-3777.
[16] RUSU R B, Cousins S. 3D is here:Point Cloud Library (PCL)[C]//IEEE International Conference on Robotics & Automation. 2011:1-4.
[17] GRIMSON W E L, LOZANO-PEREZ T. Localizing overlapping parts by searching the interpretation tree[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1987, 9(4):469-482.
Options
Outlines

/

〈 〉