Abstract
We present a robust Global Registration technique focused on environment survey applications using laser range-finders. Our approach works under the assumption that places can be recognized by analyzing the projection of the observed points along the gravity direction. Candidate 3D matches are estimated by aligning the 2D projective representations of the acquired scans, and benefiting from the corresponding dimensional reduction. Each single candidate match is then validated exploiting the implicit empty space information associated to scans. The global reconstruction problem is modeled as a directed graph, where scan poses (nodes) are connected through matches (edges). This is exploited to compute local matches (instead of global ones) between pairs of scans that are in the same reference frame. As a consequence, both performance and recall ratio increase w.r.t. using only global matches. Additionally, the graph structure allows formulating a sparse global optimization problem that optimizes scan poses, considering simultaneously all accepted matches. Our approach is being used in production systems and has been successfully evaluated on several real datasets.
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Bay, H., Ess, A., Tuytelaars, T., Gool, L.V.: Speeded-up robust features (surf). Comput. Vis. Image Underst. 110(3), 346–359 (2008)
Bosse, M., Zlot, R.: Place recognition using keypoint voting in large 3d lidar datasets. In: ICRA (2013)
Calonder, M., Lepetit, V., Ozuysal, M., Trzcinski, T., Strecha, C., Fua, P.: Brief: computing a local binary descriptor very fast. IEEE Trans. Pattern Anal. Mach. Intell. 34(7), 1281–1298 (2012)
Collier, J., Se, S., Kotamraju, V., Jasiobedzki, P.: Real-time lidar-based place recognition using distinctive shape descriptors. In: SPIE Unmanned Systems Technology (2012)
Cummins, M., Newman, P.: Appearance-only slam at large scale with fab-map 2.0. Int. J. Robot. Res. 30(9), 1100–1123 (2011)
Filliat, D.: A visual bag of words method for interactive qualitative localization and mapping. In: Proceedings 2007 IEEE International Conference on Robotics and Automation, pp. 3921–3926, April 2007
Geiger, A., Lenz, P., Urtasun, R.: Are we ready for autonomous driving? the kitti vision benchmark suite. In: CVPR (2012)
Ho, K.L., Newman, P.: Detecting loop closure with scene sequences. Int. J. Comput. Vis. 74(3), 261–286 (2007)
Košecká, J., Li, F., Yang, X.: Global localization and relative positioning based on scale-invariant keypoints. Robot. Auton. Syst. 52(1), 27–38 (2005)
Kümmerle, R., Grisetti, G., Strasdat, H., Konolige, K., Burgard, W.: G2o: a general framework for graph optimization. In: ICRA (2011)
Lee, C.H., Varshney, A., Jacobs, D.W.: Mesh saliency. ACM Trans. Graph. 24(3), 659–666 (2005)
Lowe, D.G.: Object recognition from local scale-invariant features. In: ICCV (1999)
Lynen, S., Bosse, M., Furgale, P., Siegwart, R.: Placeless place-recognition. In: 3DV (2014)
Magnusson, M., Andreasson, H., Nüchter, A., Lilienthal, A.J.: Automatic appearance-based loop detection from three-dimensional laser data using the normal distributions transform. J. Field Robot. 26(11–12), 892–914 (2009)
Muhammad, N., Lacroix, S.: Loop closure detection using small-sized signatures from 3d lidar data. In: 2011 IEEE International Symposium on Safety, Security, and Rescue Robotics, pp. 333–338, November 2011
Newman, P., Sibley, G., Smith, M., Cummins, M., Harrison, A., Mei, C., Posner, I., Shade, R., Schroeter, D., Murphy, L., Churchill, W., Cole, D., Reid, I.: Navigating, recognizing and describing urban spaces with vision and lasers. Int. J. Rob. Res. 28(11–12), 1406–1433 (2009)
Paul, R., Newman, P.: Fab-map 3d: Topological mapping with spatial and visual appearance. In: ICRA (2010)
Rusu, R.B., Cousins, S.: 3d is here: Point cloud library (PCL). In: ICRA (2011)
Rusu, R.B., Blodow, N., Beetz, M.: Fast point feature histograms (FPFH) for 3D registration. In: ICRA (2009)
Sánchez-Belenguer, C., Vendrell-Vidal, E.: An efficient technique to recompose archaeological artifacts from fragments. In: VSMM (2014)
Schindler, G., Brown, M., Szeliski, R.: City-scale location recognition. In: CVPR, pp. 1–7 (2007)
Shi, J., Tomasi, C.: Good features to track. In: CVPR (1994)
Sipiran, I., Bustos, B.: Harris 3D: a robust extension of the harris operator for interest point detection on 3D meshes. Vis. Comput. 27(11), 963–976 (2011)
Steder, B., Ruhnke, M., Grzonka, S., Burgard, W.: Place recognition in 3D scans using a combination of bag of words and point feature based relative pose estimation. In: IROS (2011)
Steder, B., Rusu, R.B., Konolige, K., Burgard, W.: Point feature extraction on 3D range scans taking into account object boundaries. In: ICRA (2011)
Taddei, P., Sánchez, C., Rodríguez, A.L., Ceriani, S., Sequeira, V.: Detecting ambiguity in localization problems using depth sensors. In: 3DV (2014)
Tombari, F., Salti, S., Di Stefano, L.: Unique signatures of histograms for local surface description. In: ECCV (2010)
Yao, J., Ruggeri, M., Taddei, P., Sequeira, V.: Robust surface registration using n-points approximate congruent sets. EURASIP J. Adv. Signal Process. (2011)
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Sánchez, C., Ceriani, S., Taddei, P., Wolfart, E., Sequeira, V. (2019). Global Registration of Point Clouds for Mapping. In: Strand, M., Dillmann, R., Menegatti, E., Ghidoni, S. (eds) Intelligent Autonomous Systems 15. IAS 2018. Advances in Intelligent Systems and Computing, vol 867. Springer, Cham. https://doi.org/10.1007/978-3-030-01370-7_56
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DOI: https://doi.org/10.1007/978-3-030-01370-7_56
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