Abstract
Mapping 3D environments is a fundamental yet challenging problem for mobile robot applications. Although 3D sensory data can be efficiently obtained using low-cost commercial RGB-D cameras, direct extension of the widely-adopted occupancy grids to 3D environments would cause problems, such as large storage consumption and intensive computation cost. In this paper, we propose to use a stack of 2D occupancy grids, each of which corresponds to a horizontal slice of the 3D environment at a specific height, as an efficient representation of 3D environments for indoor applications. Moreover, an existing algorithm based on Rao-Blackwellized Particle Filters (RBPF) is modified accordingly to perform simultaneous localization and mapping (SLAM) using the proposed multi-level occupancy grids (M-LOG), the entire codes of which have been made open source at https://github.com/AngelTianYu/micros_mlog. Experimental results from both simulation and real-world tests validate the effectiveness of the proposed approach in indoor environments. Computational cost of the approach scales linearly with the number of 2D map slices, making it the user’s choice the trade-off between vertical map resolution and efficiency.
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References
Dissanayake, G., Durrant-Whyte, H., Bailey, T.: A computationally efficient solution to the simultaneous localization and map building (SLAM) problem. In: ICRA 2000 Workshop on Mobile Robot Navigation and Mapping (2000)
Doucet, A., de Freitas, J.F.G., Murphy, K., Russel, S.: Rao-blackwellized particle filtering for dynamic bayesian networks. In: Proceedings of the Conference on Uncertainty in Artificial Intelligence (UAI) (2000)
Eliazar, A., Parr, R.: DP-SLAM: fast, robust simultaneous localization and mapping without predetermined landmarks. In: Proceedings of the International Conference on Artificial Intelligence (IJCAI) (2003)
Gutmann, J.-S., Konolige, K.: Incremental mapping of large cyclic environments. In: Proceedings of the International Symposium on Computational Intelligence in Robotics and Automation (CIRA) (2000)
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: Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (2003)
Montemerlo, M., Thrun, S., Koller, D., Wegbreit, B.: FastSLAM 2.0: an improved particle filtering algorithm for simultaneous localization and mapping that provably converges. In: Proceedings of the International Conference on Artificial Intelligence (IJCAI) (2003)
Montemerlo, M., Thrun, S., Koller, D., Wegbreit, B.: FastSLAM: a factored solution to simultaneous localization and mapping. In: Proceedings of the National Conference on Artificial Intelligence (AAAI) (2002)
Thrun, S.: An online mapping algorithm for teams of mobile robots. Int. J. Robot. Res. (2001)
Henry, P., Krainin, M., Herbst, E., Ren, X., Fox, D.: RGB-D mapping: using depth cameras for dense 3D modeling of indoor environments. In: The 12th International Symposium on Experimental Robotics (ISER) (2010)
Handa, A., Whelan, T., McDonald, J., Davison, A.J.: A benchmark for RGB-D visual odometry, 3D reconstruction and SLAM. In: Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), pp. 1524–1531 (2014)
Dryanovski, I., Morris, W., Xiao, J.: Multi-volume occupancy grids: an efficient probabilistic 3D mapping model for micro aerial vehicles. In: Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (2010)
Frese, U., Hirzinger, G.: Simultaneous localization and mapping - a discussion. In: Proceedings of the International Conference on Artificial Intelligence (IJCAI) (2001)
Murphy, K.: Bayesian map learning in dynamic environments. In: Proceedings of the Conference on Neural Information Processing System (NIPS), Denver, CO, USA, pp. 1015–1021 (1999)
Hähnel, D., Burgard, W., Wegbreit, B., Thrun, S.: Towards lazy data association in SLAM. In: Proceedings of the International Symposium of Robotics Research (ISRR) (2003)
Tam, G.K.L., Cheng, Z., Lai, Y., Langbein, F.C., Liu, Y., Marshall, D., Martin, R.R., Sun, X., Rosin, P.L.: Registration of 3D point clouds and meshes: a survey from rigid to nonrigid. IEEE Trans. Vis. Comput. Graph. 19(7), 1199–1217 (2013)
Wang, J., Xu, K., Liu, L., Cao, J., Liu, S., Yu, Z., Gu, X.D.: Consolidation of low-quality point clouds from outdoor scenes. Comput. Graph. Forum 32(5), 207–216 (2013). Blackwell Publishing Ltd
Hebert, M., Caillas, C., Krotkov, E., Kweon, I.S., Kanade, T.: Terrain mapping for a roving planetary explorer. In: Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), vol. 2, pp. 997–1002 (1989)
Triebel, R., Pfaff, P., Eurgard, W.: Multi-level surface maps for outdoor terrain mapping and loop closing. In: Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (2006)
Wurm, K.M., Hornung, A., Bennewitz, M., Stachniss, C., Burgard, W.: OctoMap: a probabilistic, flexible, and compact 3D map representation for robotic systems. In: Proceedings of the IEEE International Symposium on Robotics and Automation (ICRA), vol. 2 (2010)
Grisetti, G., Stachniss, C., Burgard, W.: Improveing grid-based SLAM with Rao-Blackwellized particle filters by adaptive proposals and selective resampling. In: Proceedings of the IEEE International Symposium on Robotics and Automation (ICRA), Barcelona, Spain, pp. 2443–2448 (2005)
Grisetti, G., Stachniss, C., Burgard, W.: Improved techniques for grid mapping with Rao-Blackwellized particle filters. IEEE Trans. Robot. 23(1), 34–46 (2007)
Visual 3D Environment Reconstruction for Autonomous Vehicles. http://ercim-news.ercim.eu/en95/special/visual-3d-environment-reconstruction-for-autonomous-vehicles
ROS.org. Ubuntu install of ROS Indigo. http://wiki.ros.org/indigo/Installation/Ubuntu. Accessed Aug 2015
Computer Vision Group. http://vision.in.tum.de/research/rgb-d_sensors_kinect
Acknowledgment
This work is supported by Research on Foundations of Major Applications, Research Programs of NUDT under Grant No. ZDYYJCYJ20140601 and the National Science Foundation of China under Grant No. 61221491, 61303185 and 61303068.
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Tian, Y., Huang, W., Wang, Y., Yi, X., Wang, Z., Yang, X. (2016). Multi-level Occupancy Grids for Efficient Representation of 3D Indoor Environments. In: Booth, R., Zhang, ML. (eds) PRICAI 2016: Trends in Artificial Intelligence. PRICAI 2016. Lecture Notes in Computer Science(), vol 9810. Springer, Cham. https://doi.org/10.1007/978-3-319-42911-3_43
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