Active SLAM and Loop Prediction with the Segmented Map Using Simplified Models
We previously introduced the SegSLAM algorithm, an approach to the simultaneous localization and mapping (SLAM) problem that divides the environment up into segments, or submaps, using heuristic methods.We investigate a realtime method for Active SLAM with SegSLAM, in which actions are selected in order to reduce uncertainty in both the local metric submap and the global topological map. Recent work in the area of Active SLAM has been built on the theoretical basis of information entropy. Due to the complexity of the SegSLAM belief state, as encoded in the SegMap representation, it is not feasible to estimate the expected entropy of the full belief state. Instead, we use a simplified model to heuristically select entropy-reducing actions without explicitly evaluating the full belief state.We discuss the relation of this heuristic method to the full entropy estimation method, and present results from applying our planning method in real-time onboard a mobile robot.
KeywordsMobile Robot Information Entropy Belief State Center Tunnel Loop Prediction
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- 2.Bourgault, F., Makarenko, A., Williams, S., Grocholsky, B., Durrant-Whyte, H.: Information based adaptive robotic exploration. In: IEEE/RSJ Intl. Conf. on Intelligent Robots and Systems (2002)Google Scholar
- 3.Burgard, W., Stachniss, C., Grisetti, G.: Information gain-based exploration using rao-blackwellized particle filters. In: Proc. of the Learning Workshop (2005)Google Scholar
- 5.Doucet, A., de Freitas, N., Murphy, K., Russell, S.: Rao-blackwellised particle filtering for dynamic bayesian networks. In: Proc. of the Sixteenth Conf. on Uncertainty in AI, pp. 176–183 (2000)Google Scholar
- 6.Fairfield, N.: Localization, Mapping, and Planning in 3D Environments. PhD thesis, Carnegie Mellon University, Pittsburgh, PA, USA (2009)Google Scholar
- 7.Fairfield, N., Kantor, G., Wettergreen, D.: Real-time slam with octree evidence grids for exploration in underwater tunnels. Journal of Field Robotics (2007)Google Scholar
- 9.Fox, D., Ko, J., Konolige, K., Stewart, B.: A hierarchical bayesian approach to the revisiting problem in mobile robot map building. In: Intl. Symp. of Robotic Research (2003)Google Scholar
- 11.Nabbe, B., Kumar, S., Hebert, M.: Path planning with hallucinated worlds. In: IEEE/RSJ Intl. Conf. on Intelligent Robots and Systems, vol. 4, pp. 3123–3130 (2004)Google Scholar
- 12.Roy, N., Thrun, S.: Coastal navigation with mobile robots. In: Advances in Neural Processing Systems, vol. 12, pp. 1043–1049 (1999)Google Scholar
- 13.Stachniss, C.: Exploration and Mapping with Mobile Robots. PhD thesis, University of Freiburg (2006)Google Scholar
- 14.Stachniss, C., Haehnel, D., Burgard, W.: Exploration with active loop-closing for FastSLAM. In: Proc. of the IEEE/RSJ Int. Conf. on Intelligent Robots and Systems, IROS (2004)Google Scholar