{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T03:59:32Z","timestamp":1760241572713,"version":"build-2065373602"},"reference-count":52,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2018,5,9]],"date-time":"2018-05-09T00:00:00Z","timestamp":1525824000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Real-time dense mapping systems have been developed since the birth of consumer RGB-D cameras. Currently, there are two commonly used models in dense mapping systems: truncated signed distance function (TSDF) and surfel. The state-of-the-art dense mapping systems usually work fine with small-sized regions. The generated dense surface may be unsatisfactory around the loop closures when the system tracking drift grows large. In addition, the efficiency of the system with surfel model slows down when the number of the model points in the map becomes large. In this paper, we propose to use two maps in the dense mapping system. The RGB-D images are integrated into a local surfel map. The old surfels that reconstructed in former times and far away from the camera frustum are moved from the local map to the global map. The updated surfels in the local map when every frame arrives are kept bounded. Therefore, in our system, the scene that can be reconstructed is very large, and the frame rate of our system remains high. We detect loop closures and optimize the pose graph to distribute system tracking drift. The positions and normals of the surfels in the map are also corrected using an embedded deformation graph so that they are consistent with the updated poses. In order to deal with large surface deformations, we propose a new method for constructing constraints with system trajectories and loop closure keyframes. The proposed new method stabilizes large-scale surface deformation. Experimental results show that our novel system behaves better than the prior state-of-the-art dense mapping systems.<\/jats:p>","DOI":"10.3390\/s18051493","type":"journal-article","created":{"date-parts":[[2018,5,10]],"date-time":"2018-05-10T03:48:27Z","timestamp":1525924107000},"page":"1493","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Real-Time Large-Scale Dense Mapping with Surfels"],"prefix":"10.3390","volume":"18","author":[{"given":"Xingyin","family":"Fu","sequence":"first","affiliation":[{"name":"Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"},{"name":"Key Laboratory of Opto-Electronic Information Processing, CAS, Shenyang 110016, China"},{"name":"The Key Lab of Image Understanding and Computer Vision, Shenyang 110016, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Feng","family":"Zhu","sequence":"additional","affiliation":[{"name":"Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China"},{"name":"Key Laboratory of Opto-Electronic Information Processing, CAS, Shenyang 110016, China"},{"name":"The Key Lab of Image Understanding and Computer Vision, Shenyang 110016, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qingxiao","family":"Wu","sequence":"additional","affiliation":[{"name":"Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China"},{"name":"Key Laboratory of Opto-Electronic Information Processing, CAS, Shenyang 110016, China"},{"name":"The Key Lab of Image Understanding and Computer Vision, Shenyang 110016, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yunlei","family":"Sun","sequence":"additional","affiliation":[{"name":"Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"},{"name":"Key Laboratory of Opto-Electronic Information Processing, CAS, Shenyang 110016, China"},{"name":"The Key Lab of Image Understanding and Computer Vision, Shenyang 110016, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rongrong","family":"Lu","sequence":"additional","affiliation":[{"name":"Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"},{"name":"Key Laboratory of Opto-Electronic Information Processing, CAS, Shenyang 110016, China"},{"name":"The Key Lab of Image Understanding and Computer Vision, Shenyang 110016, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ruigang","family":"Yang","sequence":"additional","affiliation":[{"name":"Baidu Inc., Beijing 100193, China"},{"name":"National Engineering Laboratory of Deep Learning Technology and Application, Beijing 100193, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2018,5,9]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Klein, G., and Murray, D. (2007, January 13\u201316). Parallel tracking and mapping for small AR workspaces. Proceedings of the 6th IEEE and ACM International Symposium on Mixed and Augmented Reality (ISMAR 2007), Nara, Japan.","DOI":"10.1109\/ISMAR.2007.4538852"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1255","DOI":"10.1109\/TRO.2017.2705103","article-title":"ORB-SLAM2: An Open-Source SLAM System for Monocular, Stereo and RGB-D Cameras","volume":"33","year":"2017","journal-title":"IEEE Trans. Robot."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Newcombe, R.A., Izadi, S., Hilliges, O., Molyneaux, D., Kim, D., Davison, A.J., Kohi, P., Shotton, J., Hodges, S., and Fitzgibbon, A. (2011, January 26\u201329). KinectFusion: Real-time dense surface mapping and tracking. Proceedings of the 2011 10th IEEE International Symposium on Mixed and Augmented Reality (ISMAR), Basel, Switzerland.","DOI":"10.1109\/ISMAR.2011.6092378"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., and Davison, A. (2011, January 16\u201319). KinectFusion: Real-time 3D reconstruction and interaction using a moving depth camera. Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, Santa Barbara, CA, USA.","DOI":"10.1145\/2047196.2047270"},{"key":"ref_5","unstructured":"Whelan, T., Kaess, M., Fallon, M., Johannsson, H., Leonard, J., and McDonald, J. (2012, January 9\u201310). Kintinuous: Spatially Extended Kinectfusion. Proceedings of the Workshop on RGB-D: Advanced Reasoning with Depth Cameras, Sydney, NSW, Australia."},{"key":"ref_6","first-page":"169","article-title":"Real-time 3D reconstruction at scale using voxel hashing","volume":"32","author":"Izadi","year":"2013","journal-title":"ACM Trans. Graph."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"113","DOI":"10.1145\/2461912.2461940","article-title":"Scalable real-time volumetric surface reconstruction","volume":"32","author":"Chen","year":"2013","journal-title":"ACM Trans. Graph."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1241","DOI":"10.1109\/TVCG.2015.2459891","article-title":"Very High Frame Rate Volumetric Integration of Depth Images on Mobile Devices","volume":"21","author":"Kahler","year":"2015","journal-title":"IEEE Trans. Vis. Comput. Graph."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Steinbrucker, F., Kerl, C., and Cremers, D. (2013, January 1\u20138). Large-scale multi-resolution surface reconstruction from RGB-D sequences. Proceedings of the IEEE International Conference on Computer Vision, Tampa, FL, USA.","DOI":"10.1109\/ICCV.2013.405"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Keller, M., Lefloch, D., Lambers, M., Izadi, S., Weyrich, T., and Kolb, A. (July, January 29). Real-time 3D reconstruction in dynamic scenes using point-based fusion. Proceedings of the 2013 IEEE International Conference on 3D Vision-3DV, Seattle, WA, USA.","DOI":"10.1109\/3DV.2013.9"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Whelan, T., Leutenegger, S., Salas-Moreno, R., Glocker, B., and Davison, A. (2015, January 13\u201317). ElasticFusion: Dense SLAM without a pose graph. Proceedings of the Robotics: Science and Systems, Rome, Italy.","DOI":"10.15607\/RSS.2015.XI.001"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1697","DOI":"10.1177\/0278364916669237","article-title":"ElasticFusion: Real-time dense SLAM and light source estimation","volume":"35","author":"Whelan","year":"2016","journal-title":"Int. J. Robot. Res."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"2389","DOI":"10.1109\/TVCG.2017.2734458","article-title":"Dense Visual SLAM with Probabilistic Surfel Map","volume":"23","author":"Yan","year":"2017","journal-title":"IEEE Trans. Vis. Comput. Graph."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Park, C., Kim, S., Moghadam, P., Fookes, C., and Sridharan, S. (2017, January 22\u201329). Probabilistic Surfel Fusion for Dense LiDAR Mapping. Proceedings of the 2017 IEEE Conference on Computer Vision Workshop (ICCVW), Venice, Italy.","DOI":"10.1109\/ICCVW.2017.285"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"137","DOI":"10.1016\/j.jvcir.2013.02.008","article-title":"Multi-resolution surfel maps for efficient dense 3D modeling and tracking","volume":"25","author":"Behnke","year":"2014","journal-title":"J. Vis. Commun. Image Represent."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"647","DOI":"10.1177\/0278364911434148","article-title":"RGB-D mapping: Using Kinect-style depth cameras for dense 3D modeling of indoor environments","volume":"31","author":"Henry","year":"2012","journal-title":"Int. J. Robot. Res."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Puri, P., Jia, D., and Kaess, M. (2017, January 24\u201328). GravityFusion: Real-time dense mapping without pose graph using deformation and orientation. Proceedings of the 2017 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), Vancouver, BC, Canada.","DOI":"10.1109\/IROS.2017.8206559"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"80","DOI":"10.1145\/1276377.1276478","article-title":"Embedded deformation for shape manipulation","volume":"26","author":"Sumner","year":"2007","journal-title":"ACM Trans. Graph."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Park, C., Moghadam, P., Kim, S., Elfes, A., Fookes, C., and Sridharan, S. (arXiv, 2017). Elastic LiDAR Fusion: Dense Map-Centric Continuous-Time SLAM, arXiv.","DOI":"10.1109\/ICRA.2018.8462915"},{"key":"ref_20","unstructured":"Eigen, D., Puhrsch, C., and Fergus, R. (2014, January 8\u201313). Prediction from a single image using a multi-scale deep network. Proceedings of the Conference on Neural Information Processing Systems (NIPS), Montreal, QC, Canada."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Eigen, D., and Fergus, R. (2015, January 7\u201313). Predicting depth, surface normals and semantic labels with a common multi-scale convolutional architecture. Proceedings of the IEEE International Conference on Computer Vision, Santiago, Chile.","DOI":"10.1109\/ICCV.2015.304"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Laina, I., Rupprecht, C., Belagiannis, V., Tombari, F., and Navab, N. (2016, January 10\u201312). Deeper depth prediction with fully convolutional residual networks. Proceedings of the 2016 IEEE Fourth International Conference on 3D Vision (3DV), Qingdao, China.","DOI":"10.1109\/3DV.2016.32"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Tateno, K., Tombari, F., Laina, I., and Navab, N. (arXiv, 2017). CNN-SLAM: Real-time dense monocular SLAM with learned depth prediction, arXiv.","DOI":"10.1109\/CVPR.2017.695"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Engel, J., Sch\u00f6ps, T., and Cremers, D. (2014, January 6\u201312). LSD-SLAM: Large-scale direct monocular SLAM. Proceedings of the European Conference on Computer Vision, Zurich, Switzerland.","DOI":"10.1007\/978-3-319-10605-2_54"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"8547","DOI":"10.3390\/s140508547","article-title":"A comparative study of registration methods for RGB-D video of static scenes","volume":"14","author":"Cazorla","year":"2014","journal-title":"Sensors"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"239","DOI":"10.1109\/34.121791","article-title":"A Method for registration of 3-D shapes","volume":"14","author":"Besl","year":"1992","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_27","unstructured":"Rusinkiewicz, S., and Levoy, M. (June, January 28). Efficient variants of the ICP algorithm. Proceedings of the Third IEEE International Conference on 3-D Digital Imaging and Modeling, Quebec City, QC, Canada."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"776","DOI":"10.1109\/TIP.2015.2507445","article-title":"Articulated and generalized gaussian kernel correlation for human pose estimation","volume":"25","author":"Ding","year":"2016","journal-title":"IEEE Trans. Image Process."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Ding, M., and Fan, G. (2015, January 6\u20139). Generalized sum of Gaussians for real-time human pose tracking from a single depth sensor. Proceedings of the 2015 IEEE Winter Conference on Applications of Computer Vision (WACV), Big Island, HI, USA.","DOI":"10.1109\/WACV.2015.14"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Steinbr\u00fccker, F., Sturm, J., and Cremers, D. (2011, January 6\u201313). Real-time visual odometry from dense RGB-D images. Proceedings of the 2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops), Barcelona, Spain.","DOI":"10.1109\/ICCVW.2011.6130321"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Kerl, C., Sturm, J., and Cremers, D. (2013, January 6\u201310). Robust odometry estimation for RGB-D cameras. Proceedings of the 2013 IEEE International Conference on Robotics and Automation (ICRA), Karlsruhe, Germany.","DOI":"10.1109\/ICRA.2013.6631104"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Whelan, T., Johannsson, H., Kaess, M., Leonard, J.J., and McDonald, J. (2013, January 6\u201310). Robust real-time visual odometry for dense RGB-D mapping. Proceedings of the 2013 IEEE International Conference on Robotics and Automation (ICRA), Karlsruhe, Germany.","DOI":"10.1109\/ICRA.2013.6631400"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1145\/3072959.3054739","article-title":"BundleFusion: Real-time Globally Consistent 3D Reconstruction using On-the-fly Surface Reintegration","volume":"36","author":"Dai","year":"2017","journal-title":"ACM Trans. Graph."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"1147","DOI":"10.1109\/TRO.2015.2463671","article-title":"Orb-slam: A versatile and accurate monocular slam system","volume":"31","author":"Montiel","year":"2015","journal-title":"IEEE Trans. Robot."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1109\/TASE.2016.2550621","article-title":"Monocular visual\u2014Inertial state estimation with online initialization and camera\u2014IMU extrinsic calibration","volume":"14","author":"Yang","year":"2017","journal-title":"IEEE Trans. Autom. Sci. Eng."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"598","DOI":"10.1177\/0278364914551008","article-title":"Real-time large-scale dense RGB-D SLAM with volumetric fusion","volume":"34","author":"Whelan","year":"2015","journal-title":"Int. J. Robot. Res."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"1188","DOI":"10.1109\/TRO.2012.2197158","article-title":"Bags of Binary Words for Fast Place Recognition in Image Sequences","volume":"28","year":"2012","journal-title":"IEEE Trans. Robot."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"K\u00e4hler, O., Prisacariu, V.A., and Murray, D.W. (2016). Real-time large-scale dense 3D reconstruction with loop closure. European Conference on Computer Vision, Springer.","DOI":"10.1007\/978-3-319-46484-8_30"},{"key":"ref_39","unstructured":"Prisacariu, V.A., K\u00e4hler, O., Golodetz, S., Sapienza, M., Cavallari, T., Torr, P.H., and Murray, D.W. (arXiv, 2017). InfiniTAM v3: A Framework for Large-Scale 3D Reconstruction with Loop Closure, arXiv."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Kerl, C., Sturm, J., and Cremers, D. (2013, January 3\u20138). Dense visual SLAM for RGB-D cameras. Proceedings of the 2013 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), Tokyo, Japan.","DOI":"10.1109\/IROS.2013.6696650"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"571","DOI":"10.1109\/TVCG.2014.2360403","article-title":"Real-time RGB-D camera relocalization via randomized ferns for keyframe encoding","volume":"21","author":"Glocker","year":"2015","journal-title":"IEEE Trans. Vis. Comput. Graph."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"1188","DOI":"10.1109\/TRO.2012.2197158","article-title":"Bags of binary words for fast place recognition in image sequences","volume":"28","author":"Tardos","year":"2012","journal-title":"IEEE Trans. Robot."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Cavallari, T., Golodetz, S., Lord, N.A., Valentin, J., Di Stefano, L., and Torr, P.H. (2017, January 21\u201326). On-the-fly adaptation of regression forests for online camera relocalisation. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.31"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Fischler, M.A., and Bolles, R.C. (1987). Random sample consensus: A paradigm for model fitting with applications to image analysis and automated cartography. Readings in Computer Vision: Issues, Problems, Principles, and Paradigms, Morgan Kaufmann Publishers Inc.","DOI":"10.1016\/B978-0-08-051581-6.50070-2"},{"key":"ref_45","unstructured":"K\u00fcmmerle, R., Grisetti, G., Strasdat, H., Konolige, K., and Burgard, W. (2011, January 9\u201313). G2o: A general framework for graph optimization. Proceedings of the 2011 IEEE International Conference on IEEE Robotics and Automation (ICRA), Shanghai, China."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Innmann, M., Zollh\u00f6fer, M., Nie\u00dfner, M., Theobalt, C., and Stamminger, M. (2016). VolumeDeform: Real-time volumetric non-rigid reconstruction. European Conference on Computer Vision, Springer.","DOI":"10.1007\/978-3-319-46484-8_22"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Wang, S., Zuo, X., Du, C., Wang, R., Zheng, J., and Yang, R. (2018). Dynamic Non-Rigid Objects Reconstruction with a Single RGB-D Sensor. Sensors, 18.","DOI":"10.3390\/s18030886"},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Sturm, J., Engelhard, N., Endres, F., Burgard, W., and Cremers, D. (2012, January 7\u201312). A benchmark for the evaluation of RGB-D SLAM systems. Proceedings of the 2012 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), Algarve, Portugal.","DOI":"10.1109\/IROS.2012.6385773"},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Rusu, R.B., and Cousins, S. (2011, January 9\u201313). 3D is here: Point cloud library (pcl). Proceedings of the 2011 IEEE International Conference on Robotics and Automation (ICRA), Shanghai, China.","DOI":"10.1109\/ICRA.2011.5980567"},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Handa, A., Whelan, T., McDonald, J., and Davison, A.J. (June, January 31). A benchmark for RGB-D visual odometry, 3D reconstruction and SLAM. Proceedings of the 2014 IEEE international conference on Robotics and automation (ICRA), Hong Kong, China.","DOI":"10.1109\/ICRA.2014.6907054"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"314","DOI":"10.1177\/0278364914554813","article-title":"Keyframe-based visual\u2013inertial odometry using nonlinear optimization","volume":"34","author":"Leutenegger","year":"2015","journal-title":"Int. J. Robot. Res."},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"He, Y., Zhao, J., Guo, Y., He, W., and Yuan, K. (2018). PL-VIO: Tightly-Coupled Monocular Visual\u2013Inertial Odometry Using Point and Line Features. Sensors, 18.","DOI":"10.3390\/s18041159"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/18\/5\/1493\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T15:03:59Z","timestamp":1760195039000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/18\/5\/1493"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,5,9]]},"references-count":52,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2018,5]]}},"alternative-id":["s18051493"],"URL":"https:\/\/doi.org\/10.3390\/s18051493","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2018,5,9]]}}}