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OctoMap: an efficient probabilistic 3D mapping framework based on octrees

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Abstract

Three-dimensional models provide a volumetric representation of space which is important for a variety of robotic applications including flying robots and robots that are equipped with manipulators. In this paper, we present an open-source framework to generate volumetric 3D environment models. Our mapping approach is based on octrees and uses probabilistic occupancy estimation. It explicitly represents not only occupied space, but also free and unknown areas. Furthermore, we propose an octree map compression method that keeps the 3D models compact. Our framework is available as an open-source C++ library and has already been successfully applied in several robotics projects. We present a series of experimental results carried out with real robots and on publicly available real-world datasets. The results demonstrate that our approach is able to update the representation efficiently and models the data consistently while keeping the memory requirement at a minimum.

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Notes

  1. http://www.ros.org/wiki/octomap

  2. https://github.com/OctoMap/octomap/archive/v1.5.3.tar.gz

  3. http://ais.informatik.uni-freiburg.de/projects/datasets/octomap

  4. Courtesy of B. Steder, available at http://ais.informatik.uni-freiburg.de/projects/datasets/fr360/

  5. http://www.ros.org/wiki/humanoid_localization

  6. http://www.ros.org/wiki/collider

  7. http://www.ros.org/wiki/3d_navigation and http://www.ros.org/wiki/octomap_server

References

  • Amanatides, J., & Woo, A. (1987). A fast voxel traversal algorithm for ray tracing. In Proceedings of Eurographics, Amsterdam, The Netherlands.

  • Blodow, N., Goron, L., Marton, Z., Pangercic, D., Ruhr, T., Tenorth, M., & Beetz, M. (2011). Autonomous semantic mapping for robots performing everyday manipulation tasks in kitchen environments. In Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). New York: IEEE Press.

  • Botsch, M., Wiratanaya, A., & Kobbelt, L. (2002). Efficient high quality rendering of point sampled geometry. In EGRW ’02 Proceedings of the 13th Eurographics workshop on Rendering, Aire-la-Ville, Switzerland, pp. 53–64.

  • Ciocarlie, M., Hsiao, K., Jones, E. G., Chitta, S., Rusu, R. B., & Sucan, I. A. (2010). Towards reliable grasping and manipulation in household environments. In International Symposium on Experimental Robotics (ISER), Delhi.

  • Cole, D., & Newman, P. (2006). Using laser range data for 3D SLAM in outdoor environments. In Proceedings of International Conference on Robotics & Automation (ICRA), Orlando.

  • Dornhege, C., & Kleiner, A. (2011). A frontier-void-based approach for autonomous exploration in 3D. In IEEE International Symposium on Safety, Security and Rescue Robotics (SSRR), Rio de Janeiro. New York: IEEE Press.

  • Douillard, B., Underwood, J., Melkumyan, N., Singh, S., Vasudevan, S., & Brunner, C., et al. (2010). Hybrid elevation maps: 3D surface models for segmentation. In Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Taipei. New York: IEEE Press.

  • Dryanovski, I., Morris, W., & Xiao, J. (2010). 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), Nice. New York: IEEE Press.

  • Einhorn, E., Schröter, C., & Groß, H. M. (2011). Attention-driven monocular scene reconstruction for obstacle detection, robot navigation and map building. Robotics & Autonomous Systems, 59(5), 296–309.

    Article  Google Scholar 

  • Elseberg, J., Borrmann, D., & Nüchter, A. (2011). Efficient processing of large 3d point clouds. In Proceedings of the XXIII International Symposium on Information, Communication and Automation Technologies (ICAT’11), Sarajevo.

  • Endres, F., Hess, J., Engelhard, N., Sturm, J., Cremers, D., & Burgard, W. (2012). An evaluation of the RGB-D SLAM system. In Proceedings of the IEEE International Conference on Robotics & Automation (ICRA), Guangzhou. New York: IEEE Press.

  • Fairfield, N., Kantor, G., & Wettergreen, D. (2007). Real-time SLAM with octree evidence grids for exploration in underwater tunnels. Journal of Field Robotics, 24, 3–21.

    Article  Google Scholar 

  • Fournier, J., Ricard, B., & Laurendeau, D. (2007). Mapping and exploration of complex environments using persistent 3D model. In Fourth Canadian Conference on Computer and Robot Vision, Montreal, pp. 403–410.

  • Girardeau-Montaut, D., Roux, M., Marc, R., & Thibault, G. (2005). Change detection on points cloud data acquired with a ground laser scanner. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 36, 30–35.

    Google Scholar 

  • Gutmann, J. S., Fukuchi, M., & Fujita, M. (2008). 3D perception and environment map generation for humanoid robot navigation. International Journal of Robotics Research, 27(10), 1117–1134.

    Google Scholar 

  • Habbecke, M., & Kobbelt, L. (2007). A surface-growing approach to multi-view stereo reconstruction. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Providence. New York: IEEE Press.

  • Hadsell, R., Bagnell, J. A., & Hebert, M. (2009). Accurate rough terrain estimation with space-carving kernels. In Proceedings of Robotics: Science and Systems (RSS), Zaragoza.

  • Hebert, M., Caillas, C., Krotkov, E., Kweon, I. S., & Kanade, T. (1989). Terrain mapping for a roving planetary explorer. In Proceedings of the IEEE International Conference on Robotics & Automation (ICRA), Scottsdale. New York: IEEE Press.

  • Heng, L., Meier, L., Tanskanen, P., Fraundorfer, F., & Pollefeys, M. (2011). Autonomous obstacle avoidance and maneuvering on a vision-guided MAV using on-board processing. In Proceedings of the IEEE International Conference on Robotics & Automation (ICRA), Shanghai. New York: IEEE Press.

  • Hertzberg, C., Wagner, R., Birbach, O., Hammer, T., & Frese, U. (2011). Experiences in building a visual slam system from open source components. In Proceedings of the IEEE International Conference on Robotics & Automation (ICRA), Shanghai. New York: IEEE Press.

  • Hoppe, H., DeRose, T., Duchamp, T., Mcdonald, J., & Stuetzle, W. (1992). Surface reconstruction from unorganized points. SIGGRAPH Computer Graphics, 26(2), 71–78.

    Article  Google Scholar 

  • Hornung, A., Wurm, K. M., & Bennewitz, M. (2010). Humanoid robot localization in complex indoor environments. In Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Anchorage. New York: IEEE Press.

  • Hornung, A., Phillips, M., Jones, E. G., Bennewitz, M., Likhachev, M., & Chitta, S. (2012). Navigation in three-dimensional cluttered environments for mobile manipulation. In Proceedings of the IEEE International Conference on Robotics & Automation (ICRA), Minneapolis. New York: IEEE Press.

  • Kammerl, J., Blodow, N., Rusu, R. B., Gedikli, S., Beetz, M., & Steinbach, E. G. (2012). Real-time compression of point cloud streams. In Proceedings of the IEEE International Conference on Robotics & Automation (ICRA), Minneapolis. New York: IEEE Press.

  • Kelly, A., Stentz, A., Amidi, O., Bode, M., Bradley, D. M., Diaz-Calderon, A., et al. (2006). Toward reliable off road autonomous vehicles operating in challenging environments. Journal of Robotics Research, 25(5–6), 449–483.

    Google Scholar 

  • Kraetzschmar, G., Gassull, G., & Uhl, K. (2004). Probabilistic quadtrees for variable-resolution mapping of large environments. In M. I. Ribeiro & S. J. Victor (Eds.), Proceedings of the 5th IFAC/EURON Symposium on Intelligent Autonomous Vehicles, Lisbon, Portugal.

  • Krainin, M., Henry, P., Ren, X., & Fox, D. (2011). Manipulator and object tracking for in-hand 3d object modeling. Journal of Robotics Research, 30(11), 1311–1327.

    Article  Google Scholar 

  • Laine, S., & Karras, T. (2010). Efficient sparse voxel octrees. In ACM SIGGRAPH Symposium on Interactive 3D Graphics and Games, Washington, DC.

  • Magnusson, M., Duckett, T., & Lilienthal, A. J. (2007). Scan registration for autonomous mining vehicles using 3D-NDT. Journal of Field Robotics, 24(10), 803–827.

    Article  Google Scholar 

  • Maier, D., Hornung, A., & Bennewitz, M. (2012). Real-time navigation in 3d environments based on depth camera data. In Proceedings of the IEEE-RAS International Conference on Humanoid Robots (Humanoids), Osaka. New York: IEEE Press.

  • Mason, J., Ricco, S., & Parr, R. (2011). Textured occupancy grids for monocular localization without features. In Proceedings of the IEEE International Conference on Robotics & Automation (ICRA), Shanghai. New York: IEEE Press.

  • Meagher, D. (1982). Geometric modeling using octree encoding. Computer Graphics and Image Processing, 19(2), 129–147.

    Article  Google Scholar 

  • Moravec, H. (1996). Robot spatial perception by stereoscopic vision and 3D evidence grids. Technical report CMU-RI-TR-96-34. Pittsburgh: Robotics Institute.

  • Moravec, H., & Elfes, A. (1985). High resolution maps from wide angle sonar. In Proceedings of the IEEE International Conference on Robotics & Automation (ICRA) (pp. 116–121), St. Louis. New York: IEEE Press.

  • Müller, J., Kohler, N., & Burgard, W. (2011). Autonomous miniature blimp navigation with online motion planning and re-planning. In Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Nice. New York: IEEE Press.

  • Newcombe, R., Izadi, S., Hilliges, O., Molyneaux, D., Kim, D., & Davison, A., et al. (2011). KinectFusion: Real-time dense surface mapping and tracking. In 10th IEEE International Symposium on Mixed and Augmented Reality (ISMAR) (pp. 127–136). New York: IEEE Press.

  • Nüchter, A., Lingemann, K., Hertzberg, J., & Surmann, H. (2007). 6D SLAM-3D mapping outdoor environments: Research articles. Journal of Field Robotics, 24(8–9), 699–722.

    Article  MATH  Google Scholar 

  • Oßwald, S., Hornung, A., & Bennewitz, M. (2012). Improved proposals for highly accurate localization using range and vision data. In Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Vilamoura. New York: IEEE Press.

  • Pathak, K., Birk, A., Poppinga, J., & Schwertfeger, S. (2007). 3D forward sensor modeling and application to occupancy grid based sensor fusion. In Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), San Diego. New York: IEEE Press.

  • Payeur, P., Hebert, P., Laurendeau, D., & Gosselin, C. (1997). Probabilistic octree modeling of a 3-d dynamic environment. In Proceedings of the IEEE International Conference on Robotics & Automation (ICRA), Albuquerque. New York: IEEE Press.

  • Pfaff, P., Triebel, R., Stachniss, C., Lamon, P., Burgard, W., & Siegwart, R. (2007). Towards mapping of cities. In Proceedings of the IEEE International Conference on Robotics & Automation (ICRA), Rome, Italy. New York: IEEE Press.

  • Roth-Tabak, Y., & Jain, R. (1989). Building an environment model using depth information. Computer, 22(6), 85–90.

    Article  Google Scholar 

  • Ryde, J., & Hu, H. (2010). 3D mapping with multi-resolution occupied voxel lists. Autonomous Robots, 28(2), 169–185.

    Article  Google Scholar 

  • Schnabel, R., & Klein, R. (2006). Octree-based point-cloud compression. In Symposium on Point-Based Graphics 2006, Boston.

  • Shade, R., & Newman, P. (2011). Choosing where to go: Complete 3D exploration with stereo. In Proceedings of the IEEE International Conference on Robotics & Automation (ICRA), New Orleans. New York: IEEE Press.

  • Sibley, G., Mei, C., Reid, I., & Newman, P. (2009). Adaptive relative bundle adjustment. In Proceedings of Robotics Science and Systems (RSS), Seattle.

  • Smith, M., Baldwin, I., Churchill, W., Paul, R., & Newman, P. (2009). The new college vision and laser data set. International Journal for Robotics Research (IJRR), 28(5):595–599. http://dx.doi.org/10.1177/0278364909103911.

    Google Scholar 

  • Stoyanov, T., Magnusson, M., Andreasson, H., & Lilienthal, A. J. (2010). Path planning in 3d environments using the normal distributions transform. In Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Taipei. New York: IEEE Press.

  • Sturm, J., Engelhard, N., Endres, F., Burgard, W., & Cremers, D. (2012). A benchmark for the evaluation of RGB-D slam systems. In Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). New York: IEEE Press. http://cvpr.in.tum.de/data/datasets/rgbd-dataset/download.

  • Surmann, H., Nüchter, A., & Hertzberg, J. (2003). An autonomous mobile robot with a 3d laser range finder for 3d exploration and digitalization of indoor environments. Robotics and Autonomous Systems, 45(3), 181–198.

    Article  Google Scholar 

  • Triebel, R., Pfaff, P., & Burgard, W. (2006). 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), Beijing. New York: IEEE Press.

  • Weise, T., Wismer, T., Leibe, B., & Van Gool, L. (2009). In-hand scanning with online loop closure. In ICCV Workshops, Kyoto.

  • Whelan, T., Kaess, M., Fallon, M., Johannsson, H., Leonard, J., & McDonald, J. (2012). Kintinuous: Spatially extended KinectFusion. Technical report. http://hdl.handle.net/1721.1/71756.

  • Wilhelms, J., & Van Gelder, A. (1992). Octrees for faster isosurface generation. ACM Transactions on Graphics, 11(3), 201–227.

    Article  MATH  Google Scholar 

  • Wurm, K. M., Hornung, A., Bennewitz, M., Stachniss, C., & Burgard, W. (2010). OctoMap: A probabilistic, flexible, and compact 3D map representation for robotic systems. In Proceedings of the ICRA Workshop on Best Practice in 3D Perception and Modeling for Mobile Manipulation (IROS), Anchorage.

  • Wurm, K. M., Hennes, D., Holz, D., Rusu, R. B., Stachniss, C., Konolige, K., & Burgard, W. (2011). Hierarchies of octrees for efficient 3d mapping. In Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), San Francisco, CA, USA. New York: IEEE Press.

  • Yguel, M., Aycard, O., & Laugier, C. (2007a). Update policy of dense maps: Efficient algorithms and sparse representation. In Results of the International Conference Field and Service Robotics, FSR 2007, Chamonix, Vol. 42, pp. 23–33.

  • Yguel, M., Keat, C. T. M., Braillon, C., Laugier, C., & Aycard, O. (2007b). Dense mapping for range sensors: Efficient algorithms and sparse representations. In Proceedings of Robotics: Science and Systems. Cambridge: MIT Press.

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Acknowledgments

The authors would like to thank J. Müller, S. Oßwald, R.B. Rusu, R. Schmitt, and C. Sprunk for the fruitful discussions and their contributions to the OctoMap library. This work has been supported by the German Research Foundation (DFG) under contract number SFB/TR-8 and by the European Commission under grant agreement numbers FP7-248258-First-MM and FP7-600890-ROVINA.

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  1. Department of Computer Science, University of Freiburg, Georges-Koehler-Allee, 79110 , Freiburg, Germany

    Armin Hornung, Kai M. Wurm, Maren Bennewitz, Cyrill Stachniss & Wolfram Burgard

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  1. Armin Hornung
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  2. Kai M. Wurm
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Correspondence to Armin Hornung.

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Hornung, A., Wurm, K.M., Bennewitz, M. et al. OctoMap: an efficient probabilistic 3D mapping framework based on octrees. Auton Robot 34, 189–206 (2013). https://doi.org/10.1007/s10514-012-9321-0

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