Minimal Solvers for Mini-Loop Closures in 3D Multi-Scan Alignment

3D scan registration is a classical, yet a highly useful problem in the context of 3D sensors such as Kinect and Velodyne. While there are several existing methods, the techniques are usually incremental where adjacent scans are registered first to obtain the initial poses, followed by motion averaging and bundle-adjustment refinement. In this paper, we take a different approach and develop minimal solvers for jointly computing the initial poses of cameras in small loops such as 3-, 4-, and 5-cycles. Note that the classical registration of 2 scans can be done using a minimum of 3 point matches to compute 6 degrees of relative motion. On the other hand, to jointly compute the 3D registrations in n-cycles, we take 2 point matches between the first n-1 consecutive pairs (i.e., Scan 1 & Scan 2, ... , and Scan n-1 & Scan n) and 1 or 2 point matches between Scan 1 and Scan n. Overall, we use 5, 7, and 10 point matches for 3-, 4-, and 5-cycles, and recover 12, 18, and 24 degrees of transformation variables, respectively. Using simulations and real-data we show that the 3D registration using mini n-cycles are computationally efficient, and can provide alternate and better initial poses compared to standard pairwise methods.

[1]  Gary K. L. Tam,et al.  Registration of 3D Point Clouds and Meshes: A Survey from Rigid to Nonrigid , 2013, IEEE Transactions on Visualization and Computer Graphics.

[2]  Anath Fischer,et al.  3D Point Cloud Registration for Localization Using a Deep Neural Network Auto-Encoder , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[3]  Robert C. Bolles,et al.  Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography , 1981, CACM.

[4]  Wolfram Burgard,et al.  A benchmark for the evaluation of RGB-D SLAM systems , 2012, 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[5]  Uttaran Bhattacharya,et al.  Fast Multiview 3D Scan Registration Using Planar Structures , 2017, 2017 International Conference on 3D Vision (3DV).

[6]  Marc Pollefeys,et al.  A 4-point algorithm for relative pose estimation of a calibrated camera with a known relative rotation angle , 2013, 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[7]  P. Schönemann,et al.  A generalized solution of the orthogonal procrustes problem , 1966 .

[8]  Thomas A. Funkhouser,et al.  Fine-to-Coarse Global Registration of RGB-D Scans , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[9]  Karl Johan Åström,et al.  Solutions to Minimal Generalized Relative Pose Problems , 2005 .

[10]  Andrew W. Fitzgibbon,et al.  KinectFusion: Real-time dense surface mapping and tracking , 2011, 2011 10th IEEE International Symposium on Mixed and Augmented Reality.

[11]  Venu Madhav Govindu,et al.  Efficient and Robust Large-Scale Rotation Averaging , 2013, 2013 IEEE International Conference on Computer Vision.

[12]  Torsten Sattler,et al.  Semantic Visual Localization , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[13]  Venu Madhav Govindu,et al.  On Averaging Multiview Relations for 3D Scan Registration , 2014, IEEE Transactions on Image Processing.

[14]  Andriy Myronenko,et al.  Point Set Registration: Coherent Point Drift , 2009, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[15]  Vladlen Koltun,et al.  Tangent Convolutions for Dense Prediction in 3D , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[16]  Chen Liu,et al.  FloorNet: A Unified Framework for Floorplan Reconstruction from 3D Scans , 2018, ECCV.

[17]  Mikael Persson,et al.  Lambda Twist: An Accurate Fast Robust Perspective Three Point (P3P) Solver , 2018, ECCV.

[18]  Hongdong Li,et al.  The 3D-3D Registration Problem Revisited , 2007, 2007 IEEE 11th International Conference on Computer Vision.

[19]  Roland Siegwart,et al.  A novel parametrization of the perspective-three-point problem for a direct computation of absolute camera position and orientation , 2011, CVPR 2011.

[20]  Jörg Stückler,et al.  Multi-view deep learning for consistent semantic mapping with RGB-D cameras , 2017, 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

[21]  Torsten Sattler,et al.  Hybrid Camera Pose Estimation , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[22]  Vladlen Koltun,et al.  Colored Point Cloud Registration Revisited , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).

[23]  Luc Van Gool,et al.  SURF: Speeded Up Robust Features , 2006, ECCV.

[24]  Ping Wang,et al.  An efficient solution to the perspective-three-point pose problem , 2018, Comput. Vis. Image Underst..

[25]  Torsten Sattler,et al.  Minimal Solvers for Generalized Pose and Scale Estimation from Two Rays and One Point , 2016, ECCV.

[26]  Marc Pollefeys,et al.  A Minimal Case Solution to the Calibrated Relative Pose Problem for the Case of Two Known Orientation Angles , 2010, ECCV.

[27]  Paul J. Besl,et al.  A Method for Registration of 3-D Shapes , 1992, IEEE Trans. Pattern Anal. Mach. Intell..

[28]  Vincent Lepetit,et al.  An Efficient Minimal Solution for Multi-camera Motion , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).

[29]  Zuzana Kukelova,et al.  Automatic Generator of Minimal Problem Solvers , 2008, ECCV.

[30]  David Nistér,et al.  An efficient solution to the five-point relative pose problem , 2003, 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings..

[31]  Stergios I. Roumeliotis,et al.  An Efficient Algebraic Solution to the Perspective-Three-Point Problem , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[32]  Tobias Höllerer,et al.  Computing similarity transformations from only image correspondences , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[33]  Wolfram Burgard,et al.  An evaluation of the RGB-D SLAM system , 2012, 2012 IEEE International Conference on Robotics and Automation.

[34]  Li Sun,et al.  Integrating Deep Semantic Segmentation Into 3-D Point Cloud Registration , 2018, IEEE Robotics and Automation Letters.

[35]  Jiaye Wu,et al.  Neural Procedural Reconstruction for Residential Buildings , 2018, ECCV.

[36]  Anders P. Eriksson,et al.  Fast Rotation Search with Stereographic Projections for 3D Registration , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[37]  Gim Hee Lee A Minimal Solution for Non-perspective Pose Estimation from Line Correspondences , 2016, ECCV.

[38]  Yuichi Taguchi,et al.  A Theory of Minimal 3D Point to 3D Plane Registration and Its Generalization , 2013, International Journal of Computer Vision.

[39]  Henrik I. Christensen,et al.  RGB-D edge detection and edge-based registration , 2013, 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[40]  Jiaolong Yang,et al.  Go-ICP: Solving 3D Registration Efficiently and Globally Optimally , 2013, 2013 IEEE International Conference on Computer Vision.

[41]  Jörg Stückler,et al.  CPA-SLAM: Consistent plane-model alignment for direct RGB-D SLAM , 2016, 2016 IEEE International Conference on Robotics and Automation (ICRA).

[42]  Yan Lu,et al.  Robust RGB-D Odometry Using Point and Line Features , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).

[43]  Andrew W. Fitzgibbon,et al.  Real-time non-rigid reconstruction using an RGB-D camera , 2014, ACM Trans. Graph..

[44]  Vladlen Koltun,et al.  Learning Compact Geometric Features , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).

[45]  Hongdong Li,et al.  Multi-view structure computation without explicitly estimating motion , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[46]  Pedro Miraldo,et al.  A Minimal Closed-Form Solution for Multi-Perspective Pose Estimation using Points and Lines , 2018, ECCV.

[47]  Daniel Cremers,et al.  KillingFusion: Non-rigid 3D Reconstruction without Correspondences , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[48]  Zuzana Kukelova,et al.  Polynomial Eigenvalue Solutions to Minimal Problems in Computer Vision , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[49]  Hongdong Li,et al.  Five-Point Motion Estimation Made Easy , 2006, 18th International Conference on Pattern Recognition (ICPR'06).

[50]  Viktor Larsson,et al.  Polynomial Solvers for Saturated Ideals , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).

[51]  Hongdong Li A Simple Solution to the Six-Point Two-View Focal-Length Problem , 2006, ECCV.

[52]  Daniel Cremers,et al.  A Combinatorial Solution to Non-Rigid 3D Shape-to-Image Matching , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[53]  Pascal Vasseur,et al.  A Homography Formulation to the 3pt Plus a Common Direction Relative Pose Problem , 2014, ACCV.

[54]  Roland Siegwart,et al.  Finding the Exact Rotation between Two Images Independently of the Translation , 2012, ECCV.

[55]  Wolfram Burgard,et al.  3-D Mapping With an RGB-D Camera , 2014, IEEE Transactions on Robotics.

[56]  Dieter Schmalstieg,et al.  A Minimal Solution to the Generalized Pose-and-Scale Problem , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[57]  Frederik Schaffalitzky,et al.  Four Points in Two or Three Calibrated Views: Theory and Practice , 2006, International Journal of Computer Vision.