Skip to main content
SpringerLink
Log in

Hierarchical topometric representation of 3D robotic maps

  • Published:
Autonomous Robots Aims and scope Submit manuscript

Abstract

In this paper, we propose a method for generating a hierarchical, volumetric topological map from 3D point clouds. There are three basic hierarchical levels in our map: \(storey - region - volume\). The advantages of our method are reflected in both input and output. In terms of input, we accept multi-storey point clouds and building structures with sloping roofs or ceilings. In terms of output, we can generate results with metric information of different dimensionality, that are suitable for different robotics applications. The algorithm generates the volumetric representation by generating volumes from a 3D voxel occupancy map. We then add passages (connections between volumes), combine small volumes into a big region and use a 2D segmentation method for better topological representation. We evaluate our method on several freely available datasets. The experiments highlight the advantages of our approach.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

Notes

  1. If this requirement is not met, the z-axis normal could be estimated by Principal Component Analysis (PCA). There are also other methods suitable for this task, such as the Manhattan frame estimation proposed by Ghanem et al. (2015) for more complicated cases.

  2. https://github.com/UM-ARM-Lab/sdf_tools.

  3. https://robotics.shanghaitech.edu.cn/datasets/3D_topo

References

  • Adan, A., & Huber, D. (2011). 3d reconstruction of interior wall surfaces under occlusion and clutter. 2011 International Conference on 3D Imaging. Processing, Visualization and Transmission, IEEE: Modeling pp. 275–281.

  • Armeni, I., Sener, O., Zamir, A.R., Jiang, H., Brilakis, I., Fischer, M., & Savarese, S. (2016). 3d semantic parsing of large-scale indoor spaces. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 1534–1543.

  • Babacan, K., Jung, J., Wichmann, A., Jahromi, B., Shahbazi, M., Sohn, G., & Kada, M. (2016). Towards object driven floor plan extraction from laser point cloud. International Archives of the Photogrammetry, Remote Sensing & Spatial Information Sciences 41

  • Badino, H., Huber, D., & Kanade, T. (2012). Real-time topometric localization. In: 2012 IEEE International Conference on Robotics and Automation, IEEE, pp 1635–1642.

  • Blochliger, F., Fehr, M., Dymczyk, M., Schneider, T., & Siegwart, R. (2018). Topomap: Topological mapping and navigation based on visual slam maps. In: 2018 IEEE International Conference on Robotics and Automation (ICRA), IEEE, pp 1–9

  • Chang, A., Dai, A., Funkhouser, T., Halber, M., Niessner, M., Savva, M., Song, S., Zeng, A., & Zhang, Y. (2017). Matterport3d: Learning from rgb-d data in indoor environments. arXiv preprint arXiv:1709.06158

  • Chen, H., Yang, Z., Zhao, X., Weng, G., Wan, H., Luo, J., Ye, X., Zhao, Z., He, Z., & Shen Y, et al. (2020). Advanced mapping robot and high-resolution dataset. Robotics and Autonomous Systems p 103559

  • Edelsbrunner, H., Kirkpatrick, D., & Seidel, R. (1983). On the shape of a set of points in the plane. IEEE Transactions on information theory, 29(4), 551–559.

    Article  MathSciNet  Google Scholar 

  • Ghanem, B., Thabet, A., Carlos Niebles, J., & Caba Heilbron, F. (2015). Robust manhattan frame estimation from a single rgb-d image. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 3772–3780.

  • Hayne, R., Luo, R., & Berenson, D. (2016). Considering avoidance and consistency in motion planning for human-robot manipulation in a shared workspace. In: 2016 IEEE International Conference on Robotics and Automation (ICRA), IEEE, pp 3948–3954.

  • He, Z., Hou, J., & Schwertfeger, S. (2019). Furniture free mapping using 3d lidars. In: 2019 IEEE International Conference on Robotics and Biomimetics (ROBIO), IEEE.

  • Hiller, M., Qiu, C., Particke, F., Hofmann, C., & Thielecke, J. (2020). Learning topometric semantic maps from occupancy grids. arXiv preprint arXiv:2001.03676

  • Hou, J., Yuan, Y., & Schwertfeger, S. (2018). Topological area graph generation and its application to path planning. arXiv preprint arXiv:1811.05113

  • Hou, J., Yuan, Y., & Schwertfeger, S. (2019). Area graph: Generation of topological maps using the voronoi diagram. In: 2019 19th International Conference on Advanced Robotics (ICAR)

  • Khandelwal, P., Zhang, S., Sinapov, J., Leonetti, M., Thomason, J., Yang, F., et al. (2017). Bwibots: A platform for bridging the gap between ai and human-robot interaction research. The International Journal of Robotics Research, 36(5–7), 635–659.

    Article  Google Scholar 

  • Kuipers, B. (2000). The spatial semantic hierarchy. Artificial Intelligence, 119(1–2), 191–233.

    Article  MathSciNet  Google Scholar 

  • Kuipers, B., Modayil, J., Beeson, P., MacMahon, M., & Savelli, F. (2004). Local metrical and global topological maps in the hybrid spatial semantic hierarchy. In: IEEE International Conference on Robotics and Automation, 2000. Proceedings. ICRA’04. 2000, IEEE, vol 5, pp 4845–4851

  • Mielle, M., Magnusson, M., & Lilienthal, A.J. (2018). A method to segment maps from different modalities using free space layout maoris: map of ripples segmentation. In: 2018 IEEE International Conference on Robotics and Automation (ICRA), IEEE, pp 4993–4999

  • Ochmann, S., Vock, R., Wessel, R., & Klein, R. (2014). Towards the extraction of hierarchical building descriptions from 3d indoor scans. In: 3DOR, Citeseer, pp 85–92

  • Ochmann, S., Vock, R., & Klein, R. (2019). Automatic reconstruction of fully volumetric 3d building models from point clouds. arXiv preprint arXiv:1907.00631

  • Okorn, B., Xiong, X., Akinci, B., & Huber, D. (2010). Toward automated modeling of floor plans. In: Proceedings of the symposium on 3D data processing, visualization and transmission, vol 2

  • Oleynikova, H., Taylor, Z., Siegwart, R., & Nieto, J. (2018). Sparse 3d topological graphs for micro-aerial vehicle planning. In: RSJ International Conference on Intelligent Robots and Systems (IROS), pp 1–9

  • Otsu, N. (1979). A threshold selection method from gray-level histograms. IEEE transactions on systems, man, and cybernetics, 9(1), 62–66.

    Article  MathSciNet  Google Scholar 

  • Park, B., Choi, J., & Chung, W. K. (2018). Incremental hierarchical roadmap construction for efficient path planning. ETRI Journal, 40(4), 458–470.

    Article  Google Scholar 

  • Rusu, R.B., & Cousins, S. (2011). 3d is here: Point cloud library (pcl). In: 2011 IEEE international conference on robotics and automation, IEEE, pp 1–4

  • Savelli, F., & Kuipers, B. (2004). Loop-closing and planarity in topological map-building. In: 2004 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)(IEEE Cat. No. 04CH37566), IEEE, vol 2, pp 1511–1517

  • Schmuck, P., Scherer, S. A., & Zell, A. (2016). Hybrid metric-topological 3d occupancy grid maps for large-scale mapping. IFAC-PapersOnLine, 49(15), 230–235.

    Article  Google Scholar 

  • Schwertfeger, S., & Birk, A. (2013). Evaluation of map quality by matching and scoring high-level, topological map structures. In: 2013 IEEE international conference on robotics and automation, IEEE, pp 2221–2226

  • Schwertfeger, S., & Birk, A. (2015). Map evaluation using matched topology graphs. Autonomous Robots,. https://doi.org/10.1007/s10514-015-9493-5.

    Article  Google Scholar 

  • Steadman, P. (2006). Why are most buildings rectangular? Architectural Research Quarterly, 10(2), 119–130.

    Article  Google Scholar 

  • Thrun, S. (1998). Learning metric-topological maps for indoor mobile robot navigation. Artificial Intelligence, 99(1), 21–71.

    Article  Google Scholar 

  • Turner, E., & Zakhor, A. (2014). Floor plan generation and room labeling of indoor environments from laser range data. In: 2014 International Conference on Computer Graphics Theory and Applications (GRAPP), IEEE, pp 1–12

  • Vázquez-Otero, A., Faigl, J., Dormido, R., & Duro, N. (2015). Reaction diffusion voronoi diagrams: From sensors data to computing. Sensors, 15(6), 12736–12764.

    Article  Google Scholar 

  • Wurm, K.M., Stachniss, C., & Burgard, W. (2008). Coordinated multi-robot exploration using a segmentation of the environment. In: 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems, IEEE, pp 1160–1165

  • Zhao, X., Yang, Z., & Schwertfeger, S. (2020). Mapping with reflection - detection and utilization of reflection in 3d lidar scans. In: 2020 IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR), pp 27–33, https://doi.org/10.1109/SSRR50563.2020.9292595

Download references

Acknowledgements

We acknowledge the Visualization and MultiMedia Lab at University of Zurich (UZH) for the acquisition of the 3D point clouds, and colleagues in the ShanghaiTech Mobile Autonomous Robotic Systems Lab for their support to scan the rooms used we used as datasets for our evaluation.

Author information

Authors and Affiliations

  1. School of Information Science and Technology, ShanghaiTech University, Shanghai, 201210, China

    Zhenpeng He, Hao Sun, Jiawei Hou, Yajun Ha & Sören Schwertfeger

  2. School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing, 100049, China

    Hao Sun & Jiawei Hou

  3. Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences, Shanghai, 200050, China

    Hao Sun & Jiawei Hou

Authors
  1. Zhenpeng He
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hao Sun
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jiawei Hou
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Yajun Ha
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Sören Schwertfeger
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Zhenpeng He.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

He, Z., Sun, H., Hou, J. et al. Hierarchical topometric representation of 3D robotic maps. Auton Robot 45, 755–771 (2021). https://doi.org/10.1007/s10514-021-09991-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10514-021-09991-8

Keywords

Search

Navigation