Semi-Automatic Registration of a Robotic Total Station and a CAD Model Without Control Points

The accurate registration of a robotic total station with respect to a given CAD model is a crucial task in the construction industry. Common registration techniques rely on a reference network of control points in the CAD model. One must establish correspondences between control points in the CAD model and measured points in the field. Usually physical markers or natural points of interest are selected as control points. We present a user-guided algorithm for simple and efficient registration of a robotic total station with a CAD model in indoor environments without the need for control points. The user interaction is reduced to selecting a local Manhattan-like corner structure for initial model alignment; accurate registration of the device is carried out automatically. Our algorithm relies on angle and distance measurements only and, therefore, is not limited to vision based robotic total stations. In particular, we propose a new algorithm for robust Manhattan corner extraction.

[1]  Dieter Schmalstieg,et al.  Measuring Human-made Corner Structures with a Robotic Total Station using Support Points, Lines and Planes , 2017, VISIGRAPP.

[2]  Leigh Herbert Coaker Reflector-less total station measurements and their accuracy, precision and reliability , 2009 .

[3]  Maarten Weyn,et al.  A Survey of Rigid 3D Pointcloud Registration Algorithms , 2014 .

[4]  Pavel Smrz,et al.  Continuous plane detection in point-cloud data based on 3D Hough Transform , 2014, J. Vis. Commun. Image Represent..

[5]  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.

[6]  David Fofi,et al.  A review of recent range image registration methods with accuracy evaluation , 2007, Image Vis. Comput..

[7]  Reinhard Klein,et al.  Efficient RANSAC for Point‐Cloud Shape Detection , 2007, Comput. Graph. Forum.

[8]  Mehdi Hedjazi Moghari,et al.  New Algorithms in Rigid-Body Registration and Estimation of Registration Accuracy , 2008 .

[9]  Christophe Ley,et al.  Detecting outliers: Do not use standard deviation around the mean, use absolute deviation around the median , 2013 .

[10]  Gérard G. Medioni,et al.  Object modelling by registration of multiple range images , 1992, Image Vis. Comput..

[11]  Frédéric Bosché,et al.  Automated recognition of 3D CAD model objects in laser scans and calculation of as-built dimensions for dimensional compliance control in construction , 2010, Adv. Eng. Informatics.

[12]  Henrik I. Christensen,et al.  Efficient Organized Point Cloud Segmentation with Connected Components , 2013 .

[13]  Frédéric Bosché,et al.  Plane-based registration of construction laser scans with 3D/4D building models , 2012, Adv. Eng. Informatics.

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

[15]  Berthold K. P. Horn,et al.  Closed-form solution of absolute orientation using orthonormal matrices , 1988 .

[16]  Henrique Lorenzo,et al.  Automatic point cloud coarse registration using geometric keypoint descriptors for indoor scenes , 2017 .

[17]  M. Juretzko Reflektorlose Video-Tachymetrie - ein integrales Verfahren zur Erfassung geometrischer und visueller Informationen , 2004 .

[18]  Robert J. Vanderbei,et al.  Linear Programming: Foundations and Extensions , 1998, Kluwer international series in operations research and management service.

[19]  Dieter Schmalstieg,et al.  Interactive syntactic modeling with a single-point laser range finder and camera , 2013, 2013 IEEE International Symposium on Mixed and Augmented Reality (ISMAR).

[20]  Berthold K. P. Horn,et al.  Closed-form solution of absolute orientation using unit quaternions , 1987 .

[21]  Werner Lienhart,et al.  High frequent total station measurements for the monitoring of bridge vibrations , 2017 .