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Constrained RGBD-SLAM

Published online by Cambridge University Press:  02 June 2020

Sylvie Naudet-Collette Open the ORCID record for Sylvie Naudet-Collette [Opens in a new window] ,
Kathia Melbouci ,
Vincent Gay-Bellile ,
Omar Ait-Aider  and
Michel Dhome
Sylvie Naudet-Collette*
Affiliation:
CEA LIST, DRT/LIST/DIASI/SIALV, DRT/LIST/DIASI/SIALV, CEA Saclay Nano-INNOV , Gif-sur-Yvette, 91191
Kathia Melbouci
Affiliation:
CEA, LIST, Artificial intelligence Language and Vision Laboratory, F-91191 Gif-sur-Yvette, France, E-mails: kathia.melbouci@cea.fr, vincent.gay-bellile@cea.fr
Vincent Gay-Bellile
Affiliation:
CEA, LIST, Artificial intelligence Language and Vision Laboratory, F-91191 Gif-sur-Yvette, France, E-mails: kathia.melbouci@cea.fr, vincent.gay-bellile@cea.fr
Omar Ait-Aider
Affiliation:
Pascal Institut, UMR 660, Blaise Pascal University, 63000 Clermont Ferrand, France, E-mails: omar.ait-aider@uca.fr, michel.dhome@uca.fr
Michel Dhome
Affiliation:
Pascal Institut, UMR 660, Blaise Pascal University, 63000 Clermont Ferrand, France, E-mails: omar.ait-aider@uca.fr, michel.dhome@uca.fr
*
*Corresponding author. E-mail: sylvie.naudet@cea.fr
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Summary

This paper introduces a new RGBD-Simultaneous Localization And Mapping (RGBD-SLAM) based on a revisited keyframe SLAM. This solution improves the localization by combining visual and depth data in a local bundle adjustment. Then, it presents an extension of this RGBD-SLAM that takes advantage of a partial knowledge of the scene. This solution allows using a prior knowledge of the 3D model of the environment when this latter is available which drastically improves the localization accuracy. The proposed solutions called RGBD-SLAM and Constrained RGBD-SLAM are evaluated on several public benchmark datasets and on real scenes acquired by a Kinect sensor. The system works in real time on a standard central processing units and it can be useful for certain applications, such as localization of lightweight robots, UAVs, and VR helmet.

Keywords

Simultaneous localization and mappingRGBD sensorBundle adjustment3D modelRGBD-SLAM
Type
Articles
Information
Robotica , Volume 39 , Issue 2 , February 2021 , pp. 277 - 290
Copyright
Copyright © The Author(s), 2020. Published by Cambridge University Press

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References

Davison, A. J., “Real-Time Simultaneous Localisation and Mapping with a Single Camera,” International Conference on Computer Vision (IEEE, 2003).10.1109/ICCV.2003.1238654CrossRefGoogle Scholar
Mouragnon, E., Lhuillier, M., Dhome, M., Dekeyser, F. and Sayd, P., “Real Time Localization and 3d Reconstruction,” International Conference on Computer Vision and Pattern Recognition (IEEE, 2006).CrossRefGoogle Scholar
Klein, G. and Murray, D., “Parallel Tracking and Mapping for Small AR Workspaces,” International Symposium on Mixed and Augmented Reality (IEEE, 2007).CrossRefGoogle Scholar
Newcombe, R. A., Lovegrove, S. J. and Davison, A. J., “Dtam: Dense Tracking and Mapping in Real-Time,” ICCV (2011).CrossRefGoogle Scholar
Mur-Artal, R., Monteil, J. and Tardos, J. D., “Orb-slam: A versatile and accurate monocular slam system,” Trans. Robot. 31(5), 1147–1163 (2015).CrossRefGoogle Scholar
Melbouci, K., Collette, S. N., Gay-Bellile, V., Ait-Aider, O., Carrier, M. and Dhome, M., “Bundle Adjustment Revisited for SLAM with RGBD Sensors,” International Conference on Machine Vision Applications (IEEE, 2015).Google Scholar
Melbouci, K., Collette, S. N., Gay-Bellile, V., Ait-Aider, O. and Dhome, M., “Model Based RGBD SLAM,” International Conference on Image Processing (IEEE, 2016).Google Scholar
Newcombe, R. A., Izadi, S., Hilliges, O., Molyneaux, D., Kim, D., Davison, A. J., Kohi, P., Shotton, J., Hodges, S. and Fitzgibbon, A., “Kinectfusion: Real-Time Dense Surface Mapping and Tracking,” International Symposium on Mixed and Augmented Reality (IEEE, 2011).Google Scholar
Sturm, J., Engelhard, N., Endres, F., Burgard, W. and Cremers, D., “A Benchmark for the Evaluation of RGB-D SLAM Systems,” International Conference on Intelligent Robots and Systems (IEEE, 2012).Google Scholar
Henry, P., Krainin, M., Herbst, E., Ren, X. and Fox, D., “Rgb-d mapping: Using kinect-style depth cameras for dense 3d modeling of indoor environments,” Int. J. Robot. Res. 31(5), 647–663 (2012).10.1177/0278364911434148CrossRefGoogle Scholar
Huang, A. S., Bachrach, A., Henry, P., Krainin, M., Maturana, D., Fox, D. and Roy, N., “Visual Odometry and Mapping for Autonomous Flight Using an RGB-D Camera,” International Symposium on Robotics Research (IFRR, 2011).Google Scholar
Whelan, T., Kaess, M., Johannsson, H., Fallon, M., Leonard, J. J. and McDonald, J., “Real-time large-scale dense RGB-D SLAM with volumetric fusion,” Int. J. Robot. Res. 34(4–5), 598–626 (2015).CrossRefGoogle Scholar
Meilland, M. and Comport, A. I., “On Unifying Key-Frame and Voxel-Based Dense Visual SLAM at Large Scales,” International Conference on Intelligent Robots and Systems (IEEE, 2013).Google Scholar
Kerl, C., Sturm, J. and Cremers, D., “Dense Visual SLAM for RGB-D Cameras,” International Conference on Intelligent Robots and Systems (IEEE, 2013).Google Scholar
Endres, J. S. D. C. F., Hess, J. and Burgard, W., “3-d mapping with an RGB-D camera,” IEEE Trans. Robot. 30(1), 177–187 (2014).Google Scholar
Mur-Artal, R. and Tardos, J. D., “ORB-SLAM2: An open-source SLAM system for monocular, stereo with RGB-D cameras,” Trans. Robot. 33(5), 1255–1261 (2017).Google Scholar
Scherer, S. A. and Zell, A., “Efficient Onboard RGBD-SLAM for Autonomous MAVs,” International Conference on Intelligent Robots and Systems (2013).Google Scholar
Jamiruddin, R., Sari, A. O., Shabbir, J. and Anwer, T., “RGB-depth SLAM review,” CoRR, vol. abs/1805.07696 (2018).Google Scholar
Belter, D., Nowicki, M. and Skrzypczyński, P., “Accurate Map-Based RGB-D SLAM for Mobile Robots,” Robot 2015: Second Iberian Robotics Conference (Reis, L. P., Moreira, A. P., Lima, P. U., Montano, L. and Muñoz-Martinez, V., eds.) (Springer International Publishing, Cham, 2016) pp. 533–545.CrossRefGoogle Scholar
Scherer, S. A., Dube, D. and Zell, A., “Using Depth in Visual Simultaneous Localisation and Mapping,” International Conference on Robotics and Automation (IEEE, 2012).Google Scholar
Lee, K. W., Wijesoma, W. S. and Guzman, J. I., “A constrained SLAM approach to robust and accurate localisation of autonomous ground vehicles,” Robot. Auto. Syst. 55(7), 527–540 (2007).CrossRefGoogle Scholar
Fallon, M. F., Johannsson, H. and Leonard, J. J., “Efficient Scene Simulation for Robust Monte Carlo Localization Using an RGB-D Camera,” International Conference on Robotics and Automation (IEEE, 2012).Google Scholar
Fang, Z. and Scherer, S., “Real-time onboard 6dof localization of an indoor MAV in degraded visual environments using a RGB-D camera,” International Conference on Robotics and Automation (2015).Google Scholar
Winterhalter, W., Fleckenstein, F., Steder, B., Spinello, L. and Burgard, W., “Accurate Indoor Localization for RGB-D Smartphones and Tablets Given 2d Floor Plans,” International Conference on Intelligent Robots and Systems (IEEE, 2015).Google Scholar
Klein, G. and Murray, D., “Parallel Tracking and Mapping for Small AR Workspaces,” International Symposium on Mixed and Augmented Reality (2007).Google Scholar
Bay, H., Ess, A., Tuytelaars, T. and Van Gool, L., “Speeded-up robust features (surf),” Comput. Vis. Image Underst. 110, 346–359 (2008).10.1016/j.cviu.2007.09.014CrossRefGoogle Scholar
Karami, E., Prasad, S. and Shehata, M. S., “Image matching using sift, surf, BRIEF and ORB: Performance comparison for distorted images,” Newfoundland Electrical and Computer Engineering Conference (2015).Google Scholar
DeTone, D., Malisiewicz, T. and Rabinovich, A., “Self-improving visual odometry,” 2018.Google Scholar
Tamaazousti, M., Naudet-Collette, S., Gay-Bellile, V., Bourgeois, S., Besbes, B. and Dhome, M., “The constrained slam framework for non-instrumented augmented reality,” Multimedia Tools Appl. 75, 9511–9547 (2016).10.1007/s11042-015-2968-8CrossRefGoogle Scholar
Triggs, B., McLauchlan, P. F., Hartley, R. I and Fitzgibbon, A. W., “Bundle Adjustment - A Modern Synthesis,” International Conference on Computer Vision (IEEE, 2000).Google Scholar
Endres, F., Hess, J., Engelhard, N., Sturm, J., Cremers, D. and Burgard, W., “An Evaluation of the RGB-D SLAM System,” International Conference on Robotics and Automation (IEEE, 2012).Google Scholar
Wasenmüller, O., Meyer, M. and Stricker, D., “Corbs: Comprehensive RGB-D Benchmark for SLAM Using Kinect v2,” Winter Conference on Applications of Computer Vision (IEEE, 2016).Google Scholar