An axis-symmetrical optical laser triangulation system was developed by the authors to measure the inner geometry of
long pipes used in the oil industry. It has a special optical configuration able to acquire shape information of the inner
geometry of a section of a pipe from a single image frame. A collimated laser beam is pointed to the tip of a 45° conical
mirror. The laser light is reflected in such a way that a radial light sheet is formed and intercepts the inner geometry and
forms a bright laser line on a section of the inspected pipe. A camera acquires the image of the laser line through a wide
angle lens. An odometer-based triggering system is used to shot the camera to acquire a set of equally spaced images at
high speed while the device is moved along the pipe’s axis. Image processing is done in real-time (between images
acquisitions) thanks to the use of parallel computing technology. The measured geometry is analyzed to identify
corrosion damages. The measured geometry and results are graphically presented using virtual reality techniques and
devices as 3D glasses and head-mounted displays. The paper describes the measurement principles, calibration
strategies, laboratory evaluation of the developed device, as well as, a practical example of a corroded pipe used in an
industrial gas production plant.
An axis-symmetrical optical laser triangulation system was developed by the authors to measure the inner geometry of
long pipes used in the oil industry. It has a special optical configuration able to acquire shape information of the inner
geometry of a section of a pipe from a single image frame. It uses a radial light sheet and conical triangulation to
measure the inner geometry of pipes in cylindrical coordinates. A set of equally spaced images of 1024 x 1024 pixels is
acquired at 50 Hz while the device is moved along the pipe’s axis. The measured geometry is analyzed to identify
defects like corrosion damage. A GPU based processing algorithm has been developed to make the system able to
process these images and display the geometrical/measurement result in real-time. The algorithm implements an adaptive
threshold filter and a light intensity peak search using a graphic processing unit programming architecture (CUDA).
Prior to the parallel algorithms (called kernels) a texture data type is used to remap the image, converting from polar to
Cartesian coordinates, mapping angular and radial values in a 2D pixel data matrix. Radial lines are only scanned in a
limited range (256 pixels) between a minimum and a maximum radius value. The achieved image processing frequency
is about 470 frames per second (FPS) using a notebook equipped with a GTX 285m graphics card.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.