Abstract
The use of maps allows mobile robots to navigate between known points in an environment. Using maps allows to calculate routes avoiding obstacles and not being stuck in dead ends. This paper shows how to integrate 3D perceptions on a map to obtain obstacle-free paths when obstacles are not at the level of 2D sensors, but elevated. Chairs and tables usually pose a problem when one can only see the legs with a 2D laser, although they present a high hurdle with a much larger area. This approach builds a static map starting from the construction plans of a building. A long-term map is started from the static map, and updated when adding and removing furniture, or when doors are opened or closed. A short-term map represents dynamic obstacles such as people. Obstacles are perceived by merging all available information, both 2D laser and RGB-D cameras, into a compact 3D probabilistic representation. This approach is appropriate for fast deployment and long-term operations in office or domestic environments, able to adapt to changes in the environment. This work is designed for domestic environments, and has been tested in the RoboCup@home competition, where robots must navigate in an environment that changes during the tests.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
van der Zant, T., Wisspeintner, T.: RoboCup@Home: creating and benchmarking tomorrows service robot applications. In: Lima, P. (ed) Robotic Soccer, pp. 521–528. I-Tech Education and Publishing, Vienna (2007)
Lima, P.U., Nardi, D., Iocchi, L., Kraetzschmar, G., Matteucci, M.: RoCKIn@Home: benchmarking domestic robots through competitions. In: ICAR 2013, Montevideo, Uruguay (2013)
Thrun, S., Buckenz, A.: Integrating grid-based and topological maps for mobile robot navigation. In: Proceedings of the Thirteenth National Conference on Artificial Intelligence AAAI, Portland, Oregon, August 1996
Thrun, S., Burgard, W., Fox, D.: Probabilistic Robotics (Intelligent Robotics and Autonomous Agents). The MIT Press, Cambridge (2005)
Dellaert, F., Fox, D., Burgard, W., Thrun, S.: Monte carlo localization for mobile robots. In: ICRA, vol. 2, pp. 13221328 (1999)
Lenser, S., Veloso, M.: Sensor resetting localization for poorly modelled mobile robots. In: International Conference on Robotics and Automation (2000). ISBN: 0780358864
Koenig, S., Simmons, R.: Xavier: a robot navigation architecture based on partially observable markov decision process models. In: Artificial Intelligence Based Mobile Robotics: Case Studies of Successful Robot Systems, pp. 91122 (1998)
Thrun, S., Bennewitz, M., et al.: MINERVA: a second-generation museum tour-guide robot. In: Proceedings of IEEE International Conference on Robotics and Automation (ICRA99) (1999)
Biswas, J., Veloso, M.: Episodic non-Markov localization. Robot. Auton. Syst. 87, 162–176 (2016)
Biswas, J., Veloso, M.: The 1,000-km challenge: insights and quantitative and qualitative results. In: IEEE Intelligent Systems, pp. 1541–1672 (2016)
Walcott-Bryant, A., Kaess, M., Johannsson, H., Leonard, J.J.: Dynamic pose graph SLAM: long-term mapping in low dynamic environments. In: Proceedings of the 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems, Vilamoura, Portugal, pp. 1871–1878 (2012)
Santos, J.M., Krajnik, T., Fentanes, J.P., Duckett, T.: Lifelong information-driven exploration to complete and refine 4D spatio-temporal maps. IEEE Robot. Autom. Lett. PP(99), 1–14 (2016)
Montemerlo, M., Thrun, S.: Large-scale robotic 3-D mapping of urban structures. In: Experimental Robotics IX. Springer Tracts in Advanced Robotics, vol. 21, pp 141–150 (2006)
Hornung, A., Wurm, K., Bennewitz, M., Stachniss, C., and Burgard, W.: OctoMap: an efficient probabilistic 3D mapping framework based on octrees. Auton. Robots (2013)
Martín, F., Matelln, Lera, F.: Multi-thread impact on the performance of Monte Carlo based algorithms for self-localization of robots using RGB-D sensors. In: Workshop on Physical Agents (2016)
Dellaert, F., Fox, D., Burgard, W., Thrun, S.: Monte Carlo Localization for Mobile Robots. In: IEEE International Conference on Robotics and Automation (ICRA99), May 1999
Fox, D.: KLD-Sampling: adaptive particle filters and mobile robot localization. In: Advances in Neural Information Processing Systems (NIPS) (2001)
Acknowledgment
This work has been supported by the Spanish Government TIN2016-76515-R Grant, supported with FEDER funds.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG
About this paper
Cite this paper
Ginés, J., Martín, F., Matellán, V., Lera, F.J., Balsa, J. (2018). 3D Mapping for a Reliable Long-Term Navigation. In: Ollero, A., Sanfeliu, A., Montano, L., Lau, N., Cardeira, C. (eds) ROBOT 2017: Third Iberian Robotics Conference. ROBOT 2017. Advances in Intelligent Systems and Computing, vol 694. Springer, Cham. https://doi.org/10.1007/978-3-319-70836-2_24
Download citation
DOI: https://doi.org/10.1007/978-3-319-70836-2_24
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-70835-5
Online ISBN: 978-3-319-70836-2
eBook Packages: EngineeringEngineering (R0)