Abstract
Goal-oriented acting in dynamic environments is a challenging task for a mobile robot. A fundamental problem to be solved is to map the environment during exploration. Since everyday, environments are typically not static, landmarks can occur and disappear at any time. Therefore, a SLAM approach must be able to cope with the characteristics of such environments. This work presents a multicriteria utility function to select landmarks for SLAM in dynamic environments. The landmark utility function takes into account the salience, the probability of reobservation, and the relevance for localization of a landmark. Taking into account these criteria, now enables the selection of landmarks for SLAM in dynamic environments. The performance of the approach is shown in a real-world experiment with a P3DX-platform in a living room environment.
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References
Thrun, S., Burgard, W., Fox, D.: Probabilistic Robotics (Intelligent Robotics and Autonomous Agents). The MIT Press (September 2005)
Tipaldi, G.D., Meyer-Delius, D., Burgard, W.: Lifelong localization in changing environments. International Journal of Robotics Research 32(14) (December 2013) 1662–1678
Milford, M., Wyeth, G.: Persistent Navigation and Mapping using a Biologically Inspired SLAM System. The International Journal of Robotics Research 29(9) (2010) 1131–1153
Andrade-Cetto, J., Sanfeliu, A.: Concurrent Map Building and Localization on Indoor Dynamic Environments. IJPRAI 16(3) (2002) 361–374
Andrade-Cetto, J., Sanfeliu, A.: Concurrent Map Building and Localization with Landmark Validation. In: ICPR. (2002) 693–696
Beinhofer, M., Müller, J., Burgard, W.: Near-optimal Landmark Selection for Mobile Robot Navigation. In: IEEE International Conference on Robotics and Automation (ICRA), Shanghai, China (2011)
Hochdorfer, S., Schlegel, C.: Landmark Rating and Selection Considering the Observability Regions. In Christensen, H.I., Groen, F., Petriu, E., eds.: Intelligent Autonomous Systems 11 - IAS-11. (2010) 143–152
Ester, M., Kriegel, H.P., Sander, J., Xu, X.: A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise. In Simoudis, E., Han, J., Fayyad, U., eds.: Proceedings of the 2nd International Conference on Knowledge Discovery and Data Mining (KDD), AAAI Press (1996) 226–231
Tran, T.N., Wehrens, R., Buydens, L.M.: Clustering multispectral images: a tutorial. Chemometrics and Intelligent Laboratory Systems 77 (2005) 3–17
Yip, A.M., Ding, C., Chan, T.F.: Dynamic cluster formation using level set methods. IEEE Transactions on Pattern Analysis and Machine Intelligence 28(6) (June 2006) 877–889
Xu, W., Duchateau, J.: A New Approach to Merging Gaussian Densities in Large Vocabulary Continuous Speech Recognition. In: IEEE Benelux Signal Processing Symposium, Leuven, Belgium (1998) 231–234
Gillner, S., Weiß, A.M., Mallot, H.A.: Visual homing in the absence of feature-based landmark information. Cognition 109(1) (Oct. 2008) 105–122
Dissanayake, G., Durrant-Whyte, H.F., Bailey, T.: A Computationally Efficient Solution to the Simultaneous Localisation and Map Building (SLAM) Problem. In: IEEE International Conference on Robotics and Automation (ICRA). (2000) 1009–1014
Fishburn, P.C.: Additive Utilities with Incomplete Product Set: Applications to Priorities and Assignments. Operations Research Society of America (ORSA) (1967)
Triantaphyllou, E.: Multi-Criteria Decision Making Methods: A Comparative Study. Applied optimization. Kluwer Academic Publishers (2000)
Hochdorfer, S., Schlegel, C.: 6 DoF SLAM using a ToF camera: The challenge of a continuously growing number of landmarks. In: International Conference on Intelligent Robots and Systems (IROS), Taipei, Taiwan (Oct. 2010) 3981–3986
Acknowledgments
This work has been conducted within the ZAFH Servicerobotik (http://www.servicerobotik-ulm.de/). The authors gratefully acknowledge the research grants of state of Baden-Württemberg and the European Union.
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Hochdorfer, S., Neumann, H., Schlegel, C. (2016). Landmark Rating and Selection for SLAM in Dynamic Environments. In: Menegatti, E., Michael, N., Berns, K., Yamaguchi, H. (eds) Intelligent Autonomous Systems 13. Advances in Intelligent Systems and Computing, vol 302. Springer, Cham. https://doi.org/10.1007/978-3-319-08338-4_30
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DOI: https://doi.org/10.1007/978-3-319-08338-4_30
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