Abstract
For the autonomous navigation of the robots in unknown environments, generation of environmental maps and 3D scene reconstruction play a significant role. Simultaneous localization and mapping (SLAM) helps the robots to perceive, plan and navigate autonomously whereas scene reconstruction helps the human supervisors to understand the scene and act accordingly during joint activities with the robots. For successful completion of these joint activities, a detailed understanding of the environment is required for human and robots to interact with each other. Generally, the robots are equipped with multiple sensors and acquire a large amount of data which is challenging to handle. In this paper we propose an efficient 3D scene reconstruction approach for such scenarios using vision and graphics based techniques. This approach can be applied to indoor, outdoor, small and large scale environments. The ultimate goal of this paper is to apply this system to joint rescue operations executed by human and robot teams by reducing a large amount of point cloud data to a smaller amount without compromising on the visual quality of the scene. From thorough experimentation, we show that the proposed system is memory and time efficient and capable to run on the processing unit mounted on the autonomous vehicle. For experimentation purposes, we use standard RGB-D benchmark dataset.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Kruijff, C.D.G.J.M.: EU FP7 NIFTi “Natural human-robot cooperation in dynamic environments”. Funded by the EU FP7 as part of its ICT program, contract #247870 (2010)
Engelhard, N., Endres, F., Hess, J., Sturm, J., Burgard, W.: Real-time 3d visual slam with a hand-held rgb-d camera. In: Proc. of the RGB-D Workshop on 3D Perception in Robotics at the European Robotics Forum, Sweden (2011)
Nüchter, A., Lingemann, K., Hertzberg, J., Surmann, H.: 6d slam - 3d mapping outdoor environments. J. Field Robotics 24, 699–722 (2007)
Kohlbrecher, S., Meyer, J., von Stryk, O., Klingauf, U.: A flexible and scalable slam system with full 3d motion estimation. In: Proc. IEEE International Symposium on Safety, Security and Rescue Robotics (SSRR). IEEE (2011)
Stückler, J., Behnke, S.: Multi-resolution surfel maps for efficient dense 3d modeling and tracking. Journal of Visual Communication and Image Representation (2013)
Sturm, J., Engelhard, N., Endres, F., Burgard, W., Cremers, D.: A benchmark for the evaluation of rgb-d slam systems. In: Proc. of the International Conference on Intelligent Robot Systems, IROS (2012)
Murphy, R., Burke, J.L.: Up from the rubble: Lessons learned about hri from search and rescue. In: Proceedings of the 49th Annual Meetings of the Human Factors and Ergonomics Society, pp. 437–441 (2005)
Kruijff, G., Colas, F., Svoboda, T., van Diggelen, J., Balmer, P., Pirri, F., Worst, R.: Designing intelligent robots for human-robot teaming in urban search & rescue. In: Proceedings of the AAAI 2012 Spring Symposium on Designing Intelligent Robots (2012)
Larochelle, B., Kruijff, G.J.M.: Multi-view operator control unit to improve situation awareness in usar missions. In: 2012 IEEE RO-MAN, pp. 1103–1108. IEEE (2012)
Burke, J., Murphy, R., Coovert, M., Riddle, D.: Moonlight in Miami: An ethnographic study of human-robot interaction in USAR. Human Computer Interaction 19, 85–116 (2004)
Burke, J., Murphy, R., Rogers, E., Lumelsky, V., Scholtz, J.: Final report for the DARPA/NSF interdisciplinary study on human-robot interaction. In: IEEE Systems, Man and Cybernetics Part C: Applications and Reviews, Special Issue on Human-Robot Interaction, vol. 34, pp. 103–112 (2004)
Casper, J., Murphy, R.: Human-robot interactions during the robot-assisted urban search and rescue response at the world trade center. IEEE Transactions on Systems, Man, and Cybernetics, Part B 33, 367–385 (2003)
Murphy, R.R., Tadokoro, S., Nardi, D., Jacoff, A., Fiorini, P., Choset, H., Erkmen, A.M.: Search and Rescue Robotics. In: Handbook of Robotics, pp. 1151–1173. Springer (2008) ISBN 978-3-540-30301-5
Murphy, R.: Tutorial – introduction to rescue robotics. In: 2011 IEEE International Symposium on Safety, Security, and Rescue Robotics, SSRR (2011)
Le, V.T., Moraru, V., Bouraqadi, N., Stinckwich, S., Bourdon, F., Nguyen, H.Q.: Issues and challenges in building a robust communication platform for usar robots (2007)
Carlson, J., Murphy, R.: How UGVs physically fail in the field. IEEE Transactions on Robotics 21, 423–437 (2005)
Sugiyama, H., Tsujioka, T., Murata, M.: Autonomous chain network formation by multi-robot rescue system with ad hoc networking. In: 2010 IEEE International Workshop on Safety Security and Rescue Robotics (SSRR), pp. 1–6 (2010)
Ribeiro, C., Ferworn, A., Tran, J.: Wireless mesh network performance for urban search and rescue missions. arXiv (2010)
Couceiro, M.S., Rocha, R.P., Ferreira, N.M.: Ensuring ad hoc connectivity in distributed search with robotic darwinian particle swarms. In: 2011 IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR), pp. 284–289. IEEE (2011)
Murphy, R.: Trial by fire [rescue robots]. IEEE Robotics & Automation Magazine 11, 50–61 (2004)
Birk, A., Schwertfeger, S., Pathak, K., Vaskevicius, N.: 3d data collection at disaster city at the 2008 nist response robot evaluation exercise (rree). In: 2009 IEEE International Workshop on Safety, Security & Rescue Robotics (SSRR), pp. 1–6. IEEE (2009)
Poppinga, J., Vaskevicius, N., Birk, A., Pathak, K.: Fast plane detection and polygonalization in noisy 3d range images. In: IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2008, pp. 3378–3383 (2008)
Wiemann, T., Nuchter, A., Lingemann, K., Stiene, S., Hertzberg, J.: Automatic construction of polygonal maps from point cloud data. In: 2010 IEEE International Workshop on Safety Security and Rescue Robotics (SSRR), pp. 1–6. IEEE (2010)
Schnabel, R., Klein, R.: Octree-based point-cloud compression. In: Symposium on Point-based Graphics, pp. 111–120. The Eurographics Association (2006)
Marton, Z.C., Rusu, R.B., Beetz, M.: On Fast Surface Reconstruction Methods for Large and Noisy Datasets. In: Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), Kobe, Japan (2009)
Henry, P., Krainin, M., Herbst, E., Ren, X., Fox, D.: RGB-d mapping: Using depth cameras for dense 3D modeling of indoor environments (2011)
Huang, A.S., Bachrach, A., Henry, P., Krainin, M., Maturana, D., Fox, D., Roy, N.: Visual odometry and mapping for autonomous flight using an RGB-d camera. In: Int. Symposium on Robotics Research (ISRR), Flagstaff, Arizona, USA (2011)
Bachrach, A., Prentice, S., He, R., Henry, P., Huang, A.S., Krainin, M., Maturana, D., Fox, D., Roy, N.: Estimation, planning, and mapping for autonomous flight using an rgb-d camera in gps-denied environments. I. J. Robotic Res. 31, 1320–1343 (2012)
Bay, H., Ess, A., Tuytelaars, T., Van Gool, L.: Speeded-up robust features (surf). Comput. Vis. Image Underst. 110, 346–359 (2008)
Rusu, R.B., Cousins, S.: 3D is here: Point cloud library (PCL). In: 2011 IEEE International Conference on Robotics and Automation (ICRA), pp. 1–4. IEEE (2011)
Muja, M., Lowe, D.G.: Fast approximate nearest neighbors with automatic algorithm configuration. In: VISAPP International Conference on Computer Vision Theory and Applications, pp. 331–340 (2009)
Hornung, A., Wurm, K.M., Bennewitz, M., Stachniss, C., Burgard, W.: OctoMap: An efficient probabilistic 3D mapping framework based on octrees. Autonomous Robots (2013), Software available at http://octomap.github.com
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Riaz, Z., Linder, T., Behnke, S., Worst, R., Surmann, H. (2013). Efficient Transmission and Rendering of RGB-D Views. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2013. Lecture Notes in Computer Science, vol 8033. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41914-0_51
Download citation
DOI: https://doi.org/10.1007/978-3-642-41914-0_51
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-41913-3
Online ISBN: 978-3-642-41914-0
eBook Packages: Computer ScienceComputer Science (R0)