Automatic Registration of 3D Point Clouds and Global Poses Refinement – Contributions to Simultaneous Localization and Mapping (SLAM)
Article Sidebar
Main Article Content
Abstract
3D point cloud registration and global pose refinement are two fundamental problems when performing Simultaneous Localization and Mapping (SLAM) with LIDAR sensors. Cloud registration consists of finding coordinate transformations that locally overlap pairs of point clouds, called relative poses. In order to reference several clouds at a global origin, several relative poses need to be multiplicatively composed into absolute poses along the sensor trajectory, as relative poses are never error-free, an even more general problem arises, the drift of the sensor trajectory. To deal with this, Global Refinement Models (GRM) are used, which simultaneously refine all the poses in a trajectory. In this context, two contributions are proposed here: the first is a method for registering pairs of point clouds that integrates Fast Global Registration (FGR) and Generalized Iterative Closest Point (GICP) in a multipath and multiscale approach. To do this, each cloud in a dataset is registered in the next 3, creating a graph of poses, and each pair is successively registered in a coarse-to-fine approach. The second contribution is a linear, closed MRG capable of refining all the poses in a circuit, without the need for iterations or parameter setting. To do this, the rotations of the poses are mapped onto quaternions and interpolated using the Spherical Linear Interpolation (SLERP) technique. Then another linear optimization based on the LUM-3D model is applied. The combination of models was tested on two different datasets, one with seven point clouds obtained by Laser Scanner Terrestrial (LST) and the other with 901 clouds obtained by Laser Scanner Mobile (LSM). In both cases, the models were able to fully reconstruct the datasets and significantly reduce registration and drift errors.
Downloads
Metrics
Article Details
This work is licensed under a Creative Commons Attribution 3.0 Unported License.
Authors who publish in this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
- Authors can enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) before and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (see "The Effect of Open Access").
References
AIGER, D.; MITRA, N. J.; COHEN-OR, D. 4-points congruent sets for robust pairwise surface registration. ACM SIGGRAPH 2008 papers. p.1–10, 2008.
BABIN, P.; GIGUERE, P.; POMERLEAU, F. Analysis of robust functions for registration algorithms. 2019 International Conference on Robotics and Automation (ICRA). Anais... . p.1451–1457, 2019. IEEE.
BESL, P. J.; MCKAY, N. D. Method for registration of 3-D shapes. Sensor fusion IV: control paradigms and data structures. Anais... . v. 1611, p.586–606, 1992. Spie.
BLACK, M. J.; RANGARAJAN, A. On the unification of line processes, outlier rejection, and robust statistics with applications in early vision. International journal of computer vision, v. 19, n. 1, p. 57–91, 1996. Springer.
BORRMANN, D.; ELSEBERG, J.; LINGEMANN, K.; NÜCHTER, A.; HERTZBERG, J. Globally consistent 3D mapping with scan matching. Robotics and Autonomous Systems, v. 56, n. 2, p. 130–142, 2008. Elsevier.
BUENO, M.; BOSCHÉ, F.; GONZÁLEZ-JORGE, H.; MARTÍNEZ-SÁNCHEZ, J.; ARIAS, P. 4-Plane congruent sets for automatic registration of as-is 3D point clouds with 3D BIM models. Automation in Construction, v. 89, p. 120–134, 2018. Elsevier.
CADENA, C.; CARLONE, L.; CARRILLO, H.; et al. Past, present, and future of simultaneous localization and mapping: Toward the robust-perception age. IEEE Transactions on robotics, v. 32, n. 6, p. 1309–1332, 2016. IEEE.
CHEN, Y.; MEDIONI, G. G. Object modeling by registration of multiple range images. Image Vision Comput., v. 10, n. 3, p. 145–155, 1992.
CHOI, S.; ZHOU, Q.-Y.; KOLTUN, V. Robust reconstruction of indoor scenes. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Anais... . p.5556–5565, 2015.
DONG, Z.; YANG, B.; LIANG, F.; HUANG, R.; SCHERER, S. Hierarchical registration of unordered TLS point clouds based on binary shape context descriptor. ISPRS Journal of Photogrammetry and Remote Sensing, v. 144, p. 61–79, 2018. Elsevier.
GAVIN, H. P. The Levenberg-Marquardt algorithm for nonlinear least squares curve-fitting problems. Department of Civil and Environmental Engineering, Duke University, Academic Report, 2019.
GRISETTI, G.; KUMMERLE, R.; STACHNISS, C.; BURGARD, W. A tutorial on graph-based SLAM. IEEE Intelligent Transportation Systems Magazine, v. 2, n. 4, p. 31–43, 2010. IEEE.
HARRIS, C.; STEPHENS, M. A combined corner and edge detector. Alvey vision conference. Anais... . v. 15, p.10–5244, 1988. Citeseer.
HUBER, D. F.; HEBERT, M. Fully automatic registration of multiple 3D data sets. Image and Vision Computing, v. 21, n. 7, p. 637–650, 2003. Elsevier.
HUYNH, D. Q. Metrics for 3D rotations: Comparison and analysis. Journal of Mathematical Imaging and Vision, v. 35, n. 2, p. 155–164, 2009. Springer.
KÜMMERLE, R.; GRISETTI, G.; STRASDAT, H.; KONOLIGE, K.; BURGARD, W. g 2 o: A general framework for graph optimization. 2011 IEEE International Conference on Robotics and Automation. Anais... . p.3607–3613, 2011. IEEE.
LOWE, G. Sift-the scale invariant feature transform. Inteligence Jornal, v. 2, n. 91–110, p. 2, 2004.
LU, F.; MILIOS, E. Globally consistent range scan alignment for environment mapping. Autonomous robots, v. 4, n. 4, p. 333–349, 1997. Springer.
NEUGEBAUER, P. J. Reconstruction of real-world objects via simultaneous registration and robust combination of multiple range images. International journal of shape modeling, v. 3, n. 01n02, p. 71–90, 1997. World Scientific.
PARK, J.; ZHOU, Q.-Y.; KOLTUN, V. Colored point cloud registration revisited. Proceedings of the IEEE international conference on computer vision. Anais... . p.143–152, 2017.
PAVAN, N. L.; DOS SANTOS, D. R. A global closed-form refinement for consistent TLS data registration. IEEE Geoscience and Remote Sensing Letters, v. 14, n. 7, p. 1131–1135, 2017. IEEE.
RISTER, B.; HOROWITZ, M. A.; RUBIN, D. L. Volumetric image registration from invariant keypoints. IEEE Transactions on Image Processing, v. 26, n. 10, p. 4900–4910, 2017. IEEE.
RUSINKIEWICZ, S.; LEVOY, M. Efficient variants of the ICP algorithm. Proceedings third international conference on 3-D digital imaging and modeling. Anais... . p.145–152, 2001. IEEE.
RUSU, R. B.; BLODOW, N.; BEETZ, M. Fast point feature histograms (FPFH) for 3D registration. 2009 IEEE international conference on robotics and automation. Anais... . p.3212–3217, 2009. IEEE.
RUSU, R. B.; COUSINS, S. 3d is here: Point cloud library (pcl). 2011 IEEE international conference on robotics and automation. Anais... . p.1–4, 2011. IEEE.
SEGAL, A.; HAEHNEL, D.; THRUN, S. Generalized-icp. Robotics: science and systems. Anais... . v. 2, p.435, 2009. Seattle, WA.
SHOEMAKE, K. Animating rotation with quaternion curves. Proceedings of the 12th annual conference on Computer graphics and interactive techniques. Anais... . p.245–254, 1985.
SIPIRAN, I.; BUSTOS, B. Harris 3D: a robust extension of the Harris operator for interest point detection on 3D meshes. The Visual Computer, v. 27, n. 11, p. 963–976, 2011. Springer.
THEILER, P. W.; WEGNER, J. D.; SCHINDLER, K. Keypoint-based 4-points congruent sets–automated marker-less registration of laser scans. ISPRS journal of photogrammetry and remote sensing, v. 96, p. 149–163, 2014. Elsevier.
THEILER, P. W.; WEGNER, J. D.; SCHINDLER, K. Globally consistent registration of terrestrial laser scans via graph optimization. ISPRS journal of photogrammetry and remote sensing, v. 109, p. 126–138, 2015. Elsevier.
TORR, P. H. S.; ZISSERMAN, A. MLESAC: A new robust estimator with application to estimating image geometry. Computer vision and image understanding, v. 78, n. 1, p. 138–156, 2000. Elsevier.
TRIGGS, B.; MCLAUCHLAN, P. F.; HARTLEY, R. I.; FITZGIBBON, A. W. Bundle adjustment—a modern synthesis. International workshop on vision algorithms. Anais... . p.298–372, 1999. Springer.
VLAMINCK, M.; LUONG, H.; PHILIPS, W. Have I seen this place before? A fast and robust loop detection and correction method for 3D lidar SLAM. Sensors, v. 19, n. 1, p. 23, 2019. Multidisciplinary Digital Publishing Institute.
WEBER, T.; HÄNSCH, R.; HELLWICH, O. Automatic registration of unordered point clouds acquired by Kinect sensors using an overlap heuristic. ISPRS Journal of Photogrammetry and Remote Sensing, v. 102, p. 96–109, 2015. Elsevier.
WILLIAMS, J.; BENNAMOUN, M. Multiple view 3D registration using statistical error models. Vision modeling and visualization. Anais... . v. 99, 1999.
WILLIAMS, J.; BENNAMOUN, M. Simultaneous registration of multiple corresponding point sets. Computer Vision and Image Understanding, v. 81, n. 1, p. 117–142, 2001. Elsevier.
YANG, Z.; WANG, X.; HOU, J. A 4PCS Coarse Registration Algorithm Based on ISS Feature Points. 2021 40th Chinese Control Conference (CCC). Anais... . p.7371–7375, 2021. IEEE.
ZHONG, Y. Intrinsic shape signatures: A shape descriptor for 3d object recognition. 2009 IEEE 12th International Conference on Computer Vision Workshops, ICCV Workshops. Anais... . p.689–696, 2009. IEEE.
ZHOU, Q.-Y.; PARK, J.; KOLTUN, V. Fast global registration. European conference on computer vision. Anais... . p.766–782, 2016. Springer.
ZHOU, Q.-Y.; PARK, J.; KOLTUN, V. Open3D: A modern library for 3D data processing. arXiv preprint arXiv:1801.09847, 2018.