VecSLAM: An Efficient Vector-Based SLAM Algorithm for Indoor Environments
- 284 Downloads
In this paper, we present an efficient SLAM (Simultaneous Localization and Mapping) algorithm named VecSLAM, which localizes and builds a vector map for mobile robots in indoor environments. Compared to grid-mapping approaches, vector-based mapping algorithms require a relatively small amount of memory. Two essential operations for successful vector mapping are vector merging and loop closing. Merging methods used by traditional line segment-based mapping algorithms do not consider the sensor characteristics, which causes additional mapping error and makes it harder to close loops after navigation over a long distance. In addition, few line segment-based SLAM approaches contain loop closing methodology. We present a novel vector merging scheme based on a recursive least square estimation for robust mapping. An efficient loop closing method is also proposed, which effectively distributes the resultant mapping error throughout the loop to guarantee global map consistency. Simulation studies and experimental results show that VecSLAM is an efficient and robust online localization and mapping algorithm.
KeywordsSLAM Vector Line segment Laser range finder Mobile robot
Mathematics Subject Classifications (2000)68T40 93C85
Unable to display preview. Download preview PDF.
- 1.Brunskill, E., Roy, N.: SLAM using incremental probabilistic PCA and dimensionality reduction. In: IEEE International Conference on Robotics and Automation pp. 344–349 (2005)Google Scholar
- 7.Hähnel, D., Burgard, W., Fox, D., Thrun, S.: An efficient FastSLAM algorithm for generating maps of large-scale cyclic environments from raw laser range measurements. In: IEEE International Conference on Intelligent Robots and Systems, pp. 206–211 (2003)Google Scholar
- 9.Mázl, R., Přeučil, L.: Building a 2D environment map from laser range-finder data. In: IEEE Intelligent Vehicles Symposium, pp. 290–295. IEEE, Piscataway (2000)Google Scholar
- 10.Montemerlo, M., Thrun, S., Koller, D., Wegbreit, B.: FastSLAM: a factored solution to the simultaneous localization and mapping problem. In: Proceedings of the AAAI National Conference on Artificial Intelligence, Edmonton, 28 July–1 August 2002Google Scholar
- 11.Nguyen, V., Harati, A., Martinelli, A., Siegwart, R.: Orthogonal SLAM: a step toward lightweight indoor autonomous navigation. International Conference on Intelligent Robots and Systems pp. 5007–5012 (2006)Google Scholar
- 15.Thrun, S.: Robotic mapping: a survey. In: International Joint Conference on Artificial Intelligence, pp. 1–36 (2003)Google Scholar
- 16.Zhang, L., Ghosh, B.: Line segment based map building and localization using 2D laser rangefinder. In: International Conference on Robotics and Automation, pp. 2538–2543. IEEE, Piscataway (2000)Google Scholar