Map-merging in Multi-robot Simultaneous Localization and Mapping Process Using Two Heterogeneous Ground Robots

Document Type : Original Article

Authors

Department of Mechanical Engineering, University of Isfahan, Iran

Abstract

In this article, a fast and reliable map-merging algorithm is proposed to produce a global two dimensional map of an indoor environment in a multi-robot simultaneous localization and mapping (SLAM) process. In SLAM process, to find its way in this environment, a robot should be able to determine its position relative to a map formed from its observations. To solve this complex problem, simultaneous localization and mapping methods are required. In large and complex environments, using a single robot is not reasonable because of the error accumulation and the time required. This can explain the tendency to employ multiple robots in parallel for this task. One of the challenges in the multi-robot SLAM is the map-merging problem. A centralized algorithm for map-merging is introduced in this research based on the features of local maps and without any knowledge about robots initial or relative positions. In order to validate the proposed merging algorithm, a medium scale experiment has been set up consisting of two heterogeneous mobile robots in an indoor environment equipped with laser sensors. The results indicate that the introduced algorithm shows good performance both in accuracy and fast map-merging.

Keywords


1. Rone, W. and Ben-Tzvi, P., “Mapping, localization and motion planning in mobile multi-robotic systems,” Robotica, Vol. 31, No. 1, (2013), 1–23.
2. Fenwick, J. W., Newman, P. M., and Leonard, J. J., "Cooperative concurrent mapping and localization", in 2002 IEEE International Conference on Robotics and Automation, (2002), 1810–1817.
3. Konolige, K., Fox, D., Ortiz, C., Agno, A., Eriksen, M., Limketkai, B., Ko, J., Morisset, B., Schulz, D., Stewart, B., and Vincent, R., “Centibots: Very large scale distributed robotic teams,” Springer Tracts in Advanced Robotics, Vol. 21, No. 1, (2006), 131–140.
4. Thrun, S., “A Probabilistic On-Line Mapping Algorithm for Teams of Mobile Robots,” The International Journal of Robotics Research, Vol. 20, No. 5, (2001), 335–363.
5. Williams, S. B., Dissanayake, G., and Durrant-Whyte, H., "Towards multi-vehicle simultaneous localisation and mapping", in Proceedings 2002 IEEE International Conference on Robotics and Automation, Vol. 3, 2743–2748, (2002).
6. Thrun, S. and Liu, Y., “Multi-robot SLAM with sparse extended information filers,” Robotics Research, Vol. 15, No. 1, (2005), 254–266.
7. Carpin, S., Birk, A., and Jucikas, V., “On Map Merging,” International Journal of Robotics and Autonomous Systems, Vol. 53, No. 1, (2005), 1–14.
8. Birk, A. and Carpin, S., "Merging Occupancy Grid Maps From Multiple Robots", in Proceedings of the IEEE, Vol. 94, No. 7, 1384–1397, (2006).
9. Saeedi, S., Paull, L., Trentini, M., and Li, H., “Neural Network-Based Multiple Robot Simultaneous Localization and Mapping,” IEEE Transactions on Neural Networks, Vol. 22, No. 12, (2011), 2376–2387.
10. Saeedi, S., Paull, L., Trentini, M., Seto, M., and Li, H., "Efficient map merging using a probabilistic generalized Voronoi diagram", in 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems, 4419–4424, (2012).
11. Dinnissen, P., Givigi, S. N., and Schwartz, H. M., "Map merging of Multi-Robot SLAM using Reinforcement Learning", in 2012 IEEE International Conference on Systems, Man, and Cybernetics (SMC), 53–60, (2012).
12. Li, H., Tsukada, M., Nashashibi, F., and Parent, M., “Multivehicle cooperative local mapping: A methodology based on occupancy grid map merging,” IEEE Transactions on Intelligent Transportation Systems, Vol. 15, No. 5, (2014), 2089–2100.
13. Park, J., Sinclair, A. J., Sherrill, R. E., Doucette, E. A., and Curtis, J. W., "Map merging of rotated, corrupted, and different scale maps using rectangular features", in 2016 IEEE/ION Position, Location and Navigation Symposium (PLANS), 535–543, (2016).
14. Park, J., "A Reduced Element Map Representation and Applications: Map Merging, Path Planning, and Target Interception". PhD Thesis: Aerospace Engineering, Auburn University, (2017).
15. Ahn, J. G. and Jeon, H. S., "R-Map : A Hybrid Map Created by Maximal Rectangles", in ICCAS 2010, 1336–1339, (2010).
16. Lowe, D. G., “Distinctive image features from scale-invariant keypoints,” International journal of computer vision, Vol. 60, No. 2, (2004), 91–110.
17. Harris, C. and Stephens, M., "A combined corner and edge detector.", in Proceedings of Fourth Alvey vision conference, Vol. 15, No. 50, 147–151, (1988).
18. Fischler, M. A. and Bolles, R. C., “Random Sample Consensus: A Paradigm for Model Fitting with Applications to Image Analysis and Automated Cartography,” Communications of the ACM, Vol. 24, No. 6, (1981), 381–395.