Building a dense surface map incrementally from semi-dense point cloud and RGBimages

  • Qian-shan Li
  • Rong Xiong
  • Shoudong Huang
  • Yi-ming Huang


Building and using maps is a fundamental issue for bionic robots in field applications. A dense surface map, which offers rich visual and geometric information, is an ideal representation of the environment for indoor/outdoor localization, navigation, and recognition tasks of these robots. Since most bionic robots can use only small light-weight laser scanners and cameras to acquire semi-dense point cloud and RGB images, we propose a method to generate a consistent and dense surface map from this kind of semi-dense point cloud and RGB images. The method contains two main steps: (1) generate a dense surface for every single scan of point cloud and its corresponding image(s) and (2) incrementally fuse the dense surface of a new scan into the whole map. In step (1) edge-aware resampling is realized by segmenting the scan of a point cloud in advance and resampling each sub-cloud separately. Noise within the scan is reduced and a dense surface is generated. In step (2) the average surface is estimated probabilistically and the non-coincidence of different scans is eliminated. Experiments demonstrate that our method works well in both indoor and outdoor semi-structured environments where there are regularly shaped objects.


Bionic robot Robotic mapping Surface fusion 

CLC number



Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. Amenta, N., Bern, M., 1999. Surface reconstruction by Voronoi filtering. Discr. Comput. Geom., 22(4):481–504. [doi: 10.1007/PL00009475]MathSciNetCrossRefGoogle Scholar
  2. Amenta, N., Choi, S., Kolluri, R.K., 2001. The power crust. Proc. 6th ACM Symp. on Solid Modeling and Applications, p.249-266. [doi:10.1145/376957.376986]Google Scholar
  3. Bajaj, C.L., Bernardini, F., Xu, G., 1997. Reconstructing surfaces and functions on surfaces from unorganized three-dimensional data. Algorithmica, 19(1-2):243–261. [doi: 10.1007/PL00014418]MathSciNetCrossRefGoogle Scholar
  4. Básaca-Preciado, L.C., Sergiyenko, O.Y., Rodríguez-Quinonez, J.C., et al., 2014. Optical 3D laser measurement system for navigation of autonomous mobile robot. Opt. Lasers Eng., 54:159–169. [doi: 10.1016/j.optlaseng.2013.08.005]CrossRefGoogle Scholar
  5. Cole, D.M., Newman, P.M., 2006. Using laser range data for 3D SLAM in outdoor environments. Proc. IEEE Int. Conf. on Robotics and Automation, p.1556-1563. [doi:10.1109/ROBOT.2006.1641929]Google Scholar
  6. Crossno, P., Angel, E., 1999. Spiraling edge: fast surface reconstruction from partially organized sample points. Proc. Conf. on Visualization, p.317-324.Google Scholar
  7. Dey, T.K., Wang, L., 2013. Voronoi-based feature curves extraction for sampled singular surfaces. Comput. Graph., 37(6):659–668. [doi: 10.1016/j.cag.2013.05.014]CrossRefGoogle Scholar
  8. Dey, T.K., Giesen, J., Hudson, J., 2001. Delaunay based shape reconstruction from large data. Proc. IEEE Symp. on Parallel and Large-Data Visualization and Graphics, p.19-146. [doi:10.1109/PVGS.2001.964399]Google Scholar
  9. Dey, T.K., Dyer, R., Wang, L., 2011. Localized Cocone surface reconstruction. Comput. Graph., 35(3):483–491. [doi: 10.1016/j.cag.2011.03.014]CrossRefGoogle Scholar
  10. Dey, T.K., Ge, X., Que, Q., et al., 2012. Feature-preserving reconstruction of singular surfaces. Comput. Graph. Forum, 31(5):1787–1796. [doi: 10.1111/j.1467-8659.2012.03183.x]CrossRefGoogle Scholar
  11. Felzenszwalb, P.F., Huttenlocher, D.P., 2004. Efficient graphbased image segmentation. Int. J. Comput. Vis., 59(2):167–181. [doi: 10.1023/B:VISI.0000022288.19776.77]CrossRefGoogle Scholar
  12. Gopi, M., Krishnan, S., 2002. A fast and efficient projection-based approach for surface reconstruction. Proc. Brazilian Symp. on Computer Graphics and Image Processing, p.179-186. [doi:10.1109/SIBGRA.2002.1167141]Google Scholar
  13. Holz, D., Behnke, S., 2013. Fast range image segmentation and smoothing using approximate surface reconstruction and region growing. Proc. 12th Int. Conf. on Intelligent Autonomous Systems, p.61-73. [doi:10.1007/978-3-642-33932-5_7]Google Scholar
  14. Huang, H., Wu, S., Gong, M., et al., 2013. Edge-aware point set resampling. ACM Trans. Graph., 32(1):Article 9. [doi: 10.1145/2421636.2421645]CrossRefGoogle Scholar
  15. Lin, J., Jin, X., Wang, C., et al., 2008. Mesh composition on models with arbitrary boundary topology. IEEE Trans. Visual. Comput. Graph., 14(3):653–665. [doi: 10.1109/TVCG.2007.70632]CrossRefGoogle Scholar
  16. Lopez, M.R., Sergiyenko, O.Y., Tyrsa, V.V., et al., 2010. Optoelectronic method for structural health monitoring. Struct. Health Monit., 9(2):105–120. [doi: 10.1177/1475921709340975]CrossRefGoogle Scholar
  17. Lou, R., Pernot, J.P., Mikchevitch, A., et al., 2010. Merging enriched finite element triangle meshes for fast prototyping of alternate solutions in the context of industrial maintenance. Comput.-Aid. Des., 42(8):670–681. [doi: 10.1016/j.cad.2010.01.002]CrossRefGoogle Scholar
  18. Marton, Z.C., Rusu, R.B., Beetz, M., 2009. On fast surface reconstruction methods for large and noisy point clouds. Proc. IEEE Int. Conf. on Robotics and Automation, p.3218-3223. [doi:10.1109/ROBOT.2009.5152628]Google Scholar
  19. Maurelli, F., Droeschel, D., Wisspeintner, T., et al., 2009. A 3D laser scanner system for autonomous vehicle navigation. Proc. Int. Conf. on Advanced Robotics, p.1-6.Google Scholar
  20. Newcombe, R.A., Izadi, S., Hilliges, O., et al., 2011. Kinect-Fusion: real-time dense surface mapping and tracking. Proc. 10th IEEE Int. Symp. on Mixed and Augmented Reality, p.127-136. [doi:10.1109/ISMAR.2011.6092378]Google Scholar
  21. Nüchter, A., Lingemann, K., Hertzberg, J., et al., 2007. 6D SLAM—3D mapping outdoor environments. J. Field Robot., 24(8-9):699–722. [doi: 10.1002/rob.20209]CrossRefGoogle Scholar
  22. Pandey, G., McBride, J., Savarese, S., et al., 2010. Extrinsic calibration of a 3D laser scanner and an omnidirectional camera. Proc. 7th IFAC Symp. on Intelligent Autonomous Vehicles.Google Scholar
  23. Rusu, R.B., Marton, Z.C., Blodow, N., et al., 2008. Towards 3D point cloud based object maps for household environments. Robot. Auton. Syst., 56(11):927–941. [doi: 10.1016/j.robot.2008.08.005]CrossRefGoogle Scholar
  24. Schadler, M., Stückler, J., Behnke, S., et al., 2014. Rough terrain 3D mapping and navigation using a continuously rotating 2D laser scanner. Künstl. Intell., 28(2):93–99. [doi: 10.1007/s13218-014-0301-8]CrossRefGoogle Scholar
  25. Sheehan, M., Harrison, A., Newman, P., 2012. Selfcalibration for a 3D laser. Int. J. Robot. Res., 31(5): 675–687. [doi:10.1177/0278364911429475]CrossRefGoogle Scholar
  26. Wang, Y.B., Sheng, Y.H., Lv, G.N., et al., 2007. A Delaunaybased surface reconstrution algorithm for unorganized sampling points. J. Image Graph., 12(9):1537–1543 (in Chinese).Google Scholar
  27. Whelan, T., Kaess, M., Fallon, M., et al., 2012. Kintinuous: Spatially Extended KinectFusion. Technical Report No. MIT-CSAIL-TR-2012-020. Massachusetts Institute of Technology, USA.Google Scholar
  28. Wulf, O., Wagner, B., 2003. Fast 3D scanning methods for laser measurement systems. Proc. Int. Conf. on Control Systems and Computer Science, p.2-5.Google Scholar

Copyright information

© Journal of Zhejiang University Science Editorial Office and Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  • Qian-shan Li
    • 1
    • 3
  • Rong Xiong
    • 1
    • 3
  • Shoudong Huang
    • 2
    • 3
  • Yi-ming Huang
    • 4
  1. 1.State Key Laboratory of Industrial Control TechnologyZhejiang UniversityHangzhouChina
  2. 2.Faculty of Engineering and Information TechnologyThe University of TechnologySydneyAustralia
  3. 3.ZJU-UTS Joint Center on RoboticsZhejiang UniversityHangzhouChina
  4. 4.Department of Control Science and EngineeringZhejiang UniversityHangzhouChina

Personalised recommendations