journal article·2019-02-26Open access

Topological Map Construction Based on Region Dynamic Growing and Map Representation Method

Fei Wang, Yuqiang Liu, Ling Xiao, Chengdong Wu, Hao Chu

Applied Sciences· Volume 9· Issue 5

Abstract

In the human–machine interactive scene of the service robot, obstacle information and destination information are both required, and both kinds of information need to be saved and used at the same time. In order to solve this problem, this paper proposes a topological map construction pipeline based on regional dynamic growth and a map representation method based on the conical space model. Based on the metric map, the construction pipeline can initialize the region growth point on the trajectory of the mobile robot. Next, the topological region is divided by the region dynamic growth algorithm, the map structure is simplified by the minimum spanning tree, and the similar region is merged by the region merging algorithm. After that, the parameter TM (topological information in the map) and the parameter OM (occupied information in the map) are used to represent the topological information and the occupied information. Finally, a topological map represented by the colored picture is saved by converting to color information. It is highlighted that the topological map construction pipeline is not limited by the structure of the environment, and can be automatically adjusted according to the actual environment structure. What’s more, the topological map representation method can save two kinds of map information at the same time, which simplifies the map representation structure. The experimental results show that the map construction method is flexible, and that resources such as calculation and storage are less consumed. The map representation method is convenient to use and improves the efficiency of the map in preservation.
Source: crossref · Quality: baseline

Research context

OpenAlex topics: Robotic Path Planning Algorithms · Robotics and Sensor-Based Localization · Geographic Information Systems Studies

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

Faculty of Robot Science and Engineering, Northeastern University, Shenyang 110819, China

Funding

Natural Science Foundation of Liaoning Province · Fundamental Research Funds for the Central Universities of China

References (31)

Show source-provided references
1. A review of verbal and non-verbal human–robot interactive communication10.1016/j.robot.2014.09.031 ↗
2. Luo, R.C., and Shih, W. (2018, January 13–15). Autonomous Mobile Robot Intrinsic Navigation Based on Visual Topological Map. Proceedings of the 2018 IEEE International Symposium on Industrial Electronics (ISIE), Cairns, Australia.10.1109/ISIE.2018.8433588 ↗
3. Chung, M.J.Y., Pronobis, A., Cakmak, M., Fox, D., and Rao, R.P.N. (2016, January 9–14). Autonomous question answering with mobile robots in human-populated environments. Proceedings of the 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Daejeon, Korea.10.1109/IROS.2016.7759146 ↗
4. Jo, K., Kim, C., and Sunwoo, M. (2018). Simultaneous localization and map change update for the high definition map-based autonomous driving car. Sensors, 18.10.3390/s18093145 ↗
5. Topological map construction and scene recognition for vehicle localization10.1007/s10514-017-9638-9 ↗
6. Qing, G., Zheng, Z., and Yue, X. (2017, January 28–30). Path-planning of automated guided vehicle based on improved Dijkstra algorithm. Proceedings of the 2017 Chinese Control and Decision Conference (CCDC), Chongqing, China.10.1109/CCDC.2017.7978471 ↗
7. Design of an Autonomous Robotic Vehicle for Area Mapping and Remote Monitoring
8. Chandrasekaran, B., and Conrad, J.M. (2015, January 9–12). Human-robot collaboration: A survey. Proceedings of the 2015 Southeast Conference, Fort Lauderdale, FL, USA.10.1109/SECON.2015.7132964 ↗
9. Multi-robot deployment using topological maps10.1007/s10846-017-0471-3 ↗
10. Johnson, C. (2017). Topological Mapping and Navigation in Real-World Environments. [Master’s Thesis, University of Michigan].
11. Li, X., and Qiu, H. (2015, January 6–9). An effective laser-based approach to build topological map of unknown environment. Proceedings of the 2015 IEEE International Conference on Robotics and Biomimetics (ROBIO), Zhuhai, China.10.1109/ROBIO.2015.7418767 ↗
12. Konolige, K., Marder-Eppstein, E., and Marthi, B. (2011, January 9–13). Navigation in hybrid metric-topological maps. Proceedings of the 2011 IEEE International Conference on Robotics and Automation (ICRA), Shanghai, China.10.1109/ICRA.2011.5980074 ↗
13. Azzag, H., Lebbah, M., and Arfaoui, A. (2010, January 12–14). Map-TreeMaps: A New Approach for Hierarchical and Topological Clustering. Proceedings of the 2010 International Conference on Machine Learning and Applications (ICMLA), Washington, DC, USA.10.1109/ICMLA.2010.136 ↗
14. Liu, M., Colas, F., and Siegwart, R. (2011, January 9–13). Regional topological segmentation based on mutual information graphs. Proceedings of the 2011 IEEE International Conference on Robotics and Automation (ICRA), Shanghai, China.10.1109/ICRA.2011.5979672 ↗
15. Bandera, A., and Sandoval, F. (2009, January 12–17). Spectral clustering for feature-based metric maps partitioning in a hybrid mapping framework. Proceedings of the 2009 IEEE International Conference on Robotics and Automation (ICRA), Kobe, Japan.
16. Yu, W., and Amigoni, F. (2014, January 14–18). Standard for Robot Map Data Representation for Navigation. Proceedings of the 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Chicago, IL, USA.
17. Lau, B., Sprunk, C., and Burgard, W. (2010, January 18–22). Improved updating of Euclidean distance maps and Voronoi diagrams. Proceedings of the 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Taipei, Taiwan.10.1109/IROS.2010.5650794 ↗
18. Simultaneous Location and Mapping Through a Voronoi-diagram-based Map Representation
19. Automated creation of topological maps in unknown environments using a swarm of resource-constrained robots10.1109/LRA.2016.2523600 ↗
20. Kaleci, B., Senler, C.M., Parlaktuna, O., and Gürel, U. (2016, January 6–8). Constructing Topological Map from Metric Map Using Spectral Clustering. Proceedings of the 2016 International Conference on TOOLS with Artificial Intelligence (ICTAI), San Jose, CA, USA.10.1109/ICTAI.2015.33 ↗
21. Liu, M., Colas, F., Pomerleau, F., and Siegwart, R. (2012, January 7–12). A Markov semi-supervised clustering approach and its application in topological map extraction. Proceedings of the 2012 International Conference on Intelligent Robots and Systems (2012), Vilamoura, Algarve, Portugal.10.1109/IROS.2012.6385683 ↗
22. Ravankar, A.A., Ravankar, A., Emaru, T., and Kobayashi, Y. (2017, January 19–22). A hybrid topological mapping and navigation method for large area robot mapping. Proceedings of the 2017 Society of Instrument and Control Engineers of Japan (SICE), Kanazawa, Japan.10.23919/SICE.2017.8105770 ↗
23. Kaleci, B., Parlaktuna, O., and Gurel, U. (2018, January 2–5). A comparative study for topological map construction methods from metric map. Proceedings of the 2018 Signal Processing and Communications Applications Conference (SIU), Izmir, Turkey.10.1109/SIU.2018.8404845 ↗
24. Blochliger, F., Fehr, M., Dymczyk, M., Schneider, T., and Siegwart, R. (2018, January 21–25). Topomap: Topological mapping and navigation based on visual slam maps. Proceedings of the 2018 IEEE International Conference on Robotics and Automation (ICRA), Brisbane, QLD, Australia.10.1109/ICRA.2018.8460641 ↗
25. (2018, December 09). The Bresenham Line-Drawing Algorithm. Available online: https://www.cs.helsinki.fi/group/goa/mallinnus/lines/bresenh.html.
26. An efficient algorithm for determining the convex hull of a finite planar set10.1016/0020-0190(72)90045-2 ↗
27. (2018, December 09). map_server. Available online: http://wiki.ros.org/map_server.
28. Colleens, T., Colleens, J.J., and Ryan, D. (2007, January 27–29). Occupancy grid mapping: An empirical evaluation. Proceedings of the 2007 Mediterranean Conference on Control & Automation, Athens, Greece.10.1109/MED.2007.4433772 ↗
29. “Teach Me–Show Me”—End-User Personalization of a Smart Home and Companion Robot10.1109/THMS.2015.2445105 ↗
30. Hanbury, A. (2003, January 3–6). Circular statistics applied to colour images. Proceedings of the 2003 Computer Vision Winter Workshop, Valtice, Czech Republic.
31. Grisettiyz, G., Stachniss, C., and Burgard, W. (2005, January 18–22). Improving Grid-based SLAM with Rao-Blackwellized Particle Filters by Adaptive Proposals and Selective Resampling. Proceedings of the 2005 IEEE International Conference on Robotics and Automation, Barcelona, Spain.10.1109/ROBOT.2005.1570477 ↗