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A dense map optimization method based on common-view geometry

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Abstract

This paper focuses on noisy points filtering in dynamic reconstruction. RGBD cameras can acquire depth information directly, which is an effective way to obtain a global point cloud. However, the inevitable error leads to erroneous rebuilding, which is mainly caused by wrong depth information and dynamic disturbance. Different from algebraic methods such as median filter, a geometric method is proposed based on common-view geometry. Noisy points generate more intersections with other projection lights, which are used to distinguish incorrect points. Considering the huge amount of computation, a sampling method is adopted to avoid unnecessary calculations and a cuboid method to simplify the calculation. We also employ a hybrid strategy with two different standards to increase the robustness when filtering. By calculating the geometry relationship effectively and robustly, we get good results in removing map points with incorrectly measured depth and caused by dynamic disturbance. Compared with the algebraic methods, the proposed method can also remove dynamic objects. The performance is verified in three popular public datasets.

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References

  1. Cadena, C., Carlone, L., Carrillo, H., Latif, Y., Scaramuzza, D., Neira, J., Reid, I., Leonard, J.J.: Past, present, and future of simultaneouslocalization and mapping: toward the robust-perception age. IEEE Trans. Robot. 32(6), 1309–1332 (2016). https://doi.org/10.1109/TRO.2016.2624754

    Article  Google Scholar 

  2. Fuentes-Pacheco, J., Rendon-Mancha, J.: Visual simultaneous localization and mapping: a survey. Artif. Intell. Rev. 43(1), 55–81 (2015). https://doi.org/10.1007/s10462-012-9365-8

    Article  Google Scholar 

  3. Bailey, T., Nieto, J., Guivant, J., Stevens, M., Nebot, E.: Consistency of the EKF-SLAM algorithm. In: IEEE/RSJ International Conference on Intelligent Robots and Systems, Beijing, pp. 3562–3568 (2006). https://doi.org/10.1109/IROS.2006.281644

  4. Montemerlo, M., Stentz, A.: FastSLAM: a factored solution to the simultaneous localization and mapping problem with unknown data association. In: AAAI National Conference on Artificial Intelligence (2003). https://doi.org/10.1007/s00244-005-7058-x

  5. Liu, Y, Wang, C.: A FastSLAM based on the smooth variable structure filter for UAVs. In: 15th International Conference on Ubiquitous Robots (UR), pp. 591–596 (2018). https://doi.org/10.1109/URAI.2018.8441876

  6. Klein, G, Murray, D.: Parallel tracking and mapping for small AR workspaces. In: 6th IEEE and ACM International Symposium on Mixed and Augmented Reality, pp. 225–234 (2007). https://doi.org/10.1109/ISMAR.2007.4538852

  7. Mur-Artal, R., Montiel, J., Tardos, J.: ORB-SLAM: a versatile and accurate monocular SLAM system. IEEE Trans. Robot. 31(5), 1147–1163 (2015). https://doi.org/10.1109/TRO.2015.2463671

    Article  Google Scholar 

  8. Davison, A., Reid, I., Molton, N., Stasse, O.: MonoSLAM: real-time single camera SLAM. IEEE Trans. Pattern Anal. Mach. Intell. 29(6), 1052–1067 (2007). https://doi.org/10.1109/TPAMI.2007.1049

    Article  Google Scholar 

  9. Sumikura, S., Shibuya, M., Sakurada, K.: OpenVSLAM: a versatile visual SLAM framework. In: Proceedings of the 27th ACM International Conference on Multimedia, pp. 2292–2295 (2019). https://doi.org/10.1145/3343031.3350539.10.1145/3343031.3350539

  10. Engel, J., Schoeps, T., Cremers, D.: LSD-SLAM: large-scale direct monocular SLAM. In: European Conference on Computer Vision, pp. 834–849 (2014). https://doi.org/10.1007/978-3-319-10605-2_54

  11. Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2016). https://doi.org/10.1109/TPAMI.2017.2658577

    Article  Google Scholar 

  12. Tran, V.L., Lin, H.Y.: Accurate RGB-D camera based on structured light techniques. In: IEEE International Conference on Systems Science and Engineering, pp. 235–238 (2017). https://doi.org/10.1109/ICSSE.2017.8030872

  13. Henry, P., Krainin, M., Herbst, E., Ren, X., Fox, D.: RGB-D mapping: using kinect-style depth cameras for dense 3D modeling of indoor environments. Int. J. Robot. Res. 31(5), 647–663 (2012). https://doi.org/10.1177/0278364911434148

    Article  Google Scholar 

  14. Endres, F., Hess, J., Engelhard, N., Sturm, J., Cremers, D., Burgard, W.: An evaluationof the RGB-D SLAM system. In: IEEE International Conference on Robotics and Automation, pp. 1691–1696 (2012). https://doi.org/10.1109/ICRA.2012.6225199

  15. Li, W., Li, D., Shao, H., Xu, Y.: An RGBD-SLAM with bidirectional PnP method and fuzzy frame detection module. In: 12th Asian Control Conference, pp. 1530–1535 (2019)

  16. Scherer, S., Zell, A.: Efficient onbard RGBD-SLAM for autonomous MAVs. In: IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 1062–1068 (2013). https://doi.org/10.1109/IROS.2013.6696482

  17. Des Bouvrie, S.L.: Improving RGBD indoor mapping with IMU data, Rgbd Camera (2011)

  18. Mur-Artal, R., Tardos, J.: ORB-SLAM2: an open-source SLAM system for monocular, stereo and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2016). https://doi.org/10.1109/TRO.2017.2705103

    Article  Google Scholar 

  19. Abdul Shukor, S., Rahim, N.: A review of data structure and filtering in handling 3D big point cloud data for building preservation. In: IEEE Conference on Systems, Process and Control, pp. 141–146 (2018). https://doi.org/10.1109/SPC.2018.8704136

  20. Miliaresis, G., Kokkas, N.: Segmentation and object-based classification for the extraction of the building class from LIDAR DEMs. Comput. Geosci. 33(8), 1076–1087 (2007). https://doi.org/10.1016/j.cageo.2006.11.012

    Article  Google Scholar 

  21. Chehata, N., David, N., Bretar, F.: LIDAR data classification using hierarchical K-means clustering. ISPRS Congress Beijing, pp. 325–330 (2008)

  22. Yerokhin, A., Semenets, V.: F-transform 3D point cloud filtering algorithm. In: IEEE Second International Conference on Data Stream Mining & Processing, pp. 524–527 (2018). https://doi.org/10.1109/DSMP.2018.8478581

  23. Kurban, T., Besdok, E.: 3 dimensional point cloud filtering using differential evolution algorithm. In: IEEE Jordan International Joint Conference on Electrical Engineering and Information Technology, pp. 653–657 (2019). https://doi.org/10.1109/JEEIT.2019.8717440

  24. Hartley, R., Zisserman, A.: Multiple View Geometry in Computer Vision. Cambridge University Press, Cambridge (2003)

    MATH  Google Scholar 

  25. Fankhauser, P., Bloesch, M.: Kinect v2 for mobile robot navigation: evaluation and modeling. In: International Conference on Advanced Robotics, pp. 388–394 (2015). https://doi.org/10.1109/ICAR.2015.7251485

  26. Rublee, E., Rabaud, V.: ORB: an efficient alternative to SIFT or SURF. In: IEEE International Conference on Computer Vision, pp. 2564–2571 (2011). https://doi.org/10.1109/ICCV.2011.6126544

  27. Jamieson, K., Balakrishnan, H.: Sift: a MAC protocol for event-driven wireless sensor networks. In: European Workshop on Wireless Sensor Networks, pp. 260–275 (2003). https://doi.org/10.1007/11669463_20

  28. Bay, H., Ess, A., Tuytelaars, T., Van Gool, L.: Speeded-up robust features (SURF). In: European Conference on Computer Vision, pp. 404–417 (2008). https://doi.org/10.1016/j.cviu.2007.09.014

  29. Leutenegger, S., Chli, M.: BRISK: binary robust invariant scalable keypoints. In: IEEE International Conference on Computer Vision, pp. 2548–2555 (2011). https://doi.org/10.1109/ICCV.2011.6126542

  30. Gao, X.-S., Hou, X.-R.: Complete solution classification for the perspective-three-point problem. IEEE Trans. Pattern Anal. Mach. Intell. 25(8), 930–943 (2003). https://doi.org/10.1109/TPAMI.2003.1217599

    Article  Google Scholar 

  31. Gao, X., Zhang, T.: Robust RGB-D simultaneous localization and mapping using planar point features. Robot. Auton. Syst. 72, 1–14 (2015). https://doi.org/10.1016/j.robot.2015.03.007

    Article  Google Scholar 

  32. Kuemmerle, R., Grisetti, G.: g2o: a general framework for graph optimization. In: IEEE International Conference on Robotics and Automation, pp. 3607–3613 (2011). https://doi.org/10.1109/ICRA.2011.5979949

  33. Yu, J., Mcmillan, L.: General linear cameras. In: European Conference on Computer Vision, pp. 14–27 (2004). https://doi.org/10.1007/978-3-540-24671-8_2

  34. Silberman, N., Hoiem, D.: Indoor segmentation and support inference from RGBD images. In: European Conference on Computer Vision, pp. 746–760 (2012). https://doi.org/10.1007/978-3-642-33715-4_54

  35. Sturm, J., Engelhard, N., Endres, F.: A benchmark for the evaluation of RGB-D SLAM systems. In: IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 573–580 (2012). https://doi.org/10.1109/IROS.2012.6385773

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Acknowledgements

This work was supported by the Jiangsu Province Science and Technology Support Program of China (Grant No. BE2014712).

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  1. College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, 210016, China

    Luomin Jiang, Congqing Wang & Di Luo

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  1. Luomin Jiang
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  2. Congqing Wang
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Correspondence to Luomin Jiang.

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Jiang, L., Wang, C. & Luo, D. A dense map optimization method based on common-view geometry. SIViP 15, 1179–1187 (2021). https://doi.org/10.1007/s11760-020-01846-6

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