Calibration of Dual Laser-Based Range Cameras
for Reduced Occlusion in 3D Imaging
Aaron Mavrinac, Xiang Chen, Peter Denzinger, and Michael Sirizzotti
Abstract— A robust model-based calibration method for dual
laser line active triangulation range cameras, with the goal of
reducing camera occlusion via data fusion, is presented. The
algorithm is split into two stages: line-based estimation of the
lens distortion parameters in the individual cameras, and com-
putation of the perspective transformation from each image to a
common world frame in the laser plane using correspondences
on a target with known geometry. Experimental results are
presented, evaluating the accuracy of the calibration based on
mean position error as well as the ability of the system to reduce
camera occlusion.
I. INTRODUCTION
Active 3D vision is a popular family of methods for
obtaining robust and accurate three-dimensional digitizations
of real objects [1], [2]. One common paradigm, known as
laser line triangulation, uses a single camera to view a laser
line projected over the surface of an object at some angular
offset from its principal axis, processing the image to obtain
a cross-section profile of the object; this process can be
repeated moving the object in small increments normal to
the laser plane to yield a full surface scan of the object.
A major problem with these devices, particularly in inspec-
tion and metrology applications, is the occlusion of portions
of the target object, resulting in incomplete 3D data. Two
types of occlusion may occur: laser occlusion, in which the
laser is unable to illuminate an object point visible from
the camera, and camera occlusion, in which the camera is
unable to image an object point illuminated by the laser.
Such issues are typically overcome by performing multiple
scans or employing more complex systems, imposing a large
amount of overhead.
Camera occlusion occurs in any case where a portion of
the target surface faces away from the camera at a greater
angle from the horizontal than the camera itself (about the
x axis, in our convention described in Section II-A). Thus,
it is possible to mitigate this by adding a second camera
to the system at some angle on the opposite side of the
laser plane. In practice, most surface portions occluded from
one direction are visible from the other, with the occlusion
typically being caused by a height discontinuity of some sort;
eliminating this therefore usually yields nearly complete 3D
This research was supported by the MITACS ACCELERATE program
and the Ontario Centres of Excellence Interact initiative in collaboration
between Vista Solutions Inc. and the University of Windsor.
A. Mavrinac and X. Chen are with the Department of Electrical &
Computer Engineering, University of Windsor, 401 Sunset Ave., Windsor,
Ontario, Canada, N9B 3P4. {mavrin1,xchen}@uwindsor.ca
P. Denzinger and M. Sirizzotti are with Vista Solutions,
2835 Kew Dr., Unit #1, Windsor, Ontario, Canada, N8T 3B7.
{pdenzinger,msirizzotti}@vistasolutions.ca
data. We seek to obtain a combined range image with all
information which would otherwise be available from two
separate scans in opposite orientations.
The challenge, then, is to combine the data from both
sources in such a way that existing processes (e.g. for
inspection or metrology) can be applied to the more complete
data unmodified – in other words, the system should be a
drop-in replacement for the single-camera equivalent. We
present here a robust model-based calibration method for
two laser-based range cameras which allows this data to be
fused into a single 3D point cloud or range image in real
time.
Although model-based calibration of active triangulation
camera systems is essentially the same problem as standard
camera calibration, some techniques taking advantage of
the specifics of laser line triangulation have been proposed.
Reid [3] presents a method for estimating the projective
homography with the laser plane using correspondences
between the image and a set of orthogonal planes of known
geometry in the scene. Jokinen [4] presents an area-based
matching approach in which multiple profile maps from
different viewpoints are registered to refine an initial target-
based calibration.
Departing from model-based calibration, Trucco et al. [5]
present a direct calibration method which interpolates a
lookup table for the entire field of view based on a target of
known geometry, and thus implicitly models all intermediate
parameters; this is further explored in [6].
Vilac¸a et al. [7] present a complete calibration method
for two laser-based range cameras, also with the goal in
mind of reducing camera occlusion. It is similar to our
method in that it constrains lens distortion correction and
the perspective homography to the laser plane – a valid
simplification over traditional camera calibration, since sub-
sequent measurements are also constrained there. However,
our approach uses the range data directly for calibration,
which allows for implicit constraint of calibration to the
laser plane, higher accuracy (if range values can be obtained
with subpixel accuracy), a more direct line-based process for
lens distortion correction, and the use of a more practical
calibration apparatus.
In the general case, combining range data from multiple
sources is often achieved via registration algorithms (Salvi
et al. [8] present an excellent overview). While this approach
is well-studied and its various algorithms can be applied in a
diverse range of situations, the calibration approach has clear
advantages: it is completely unaffected by incomplete over-
lap, which in contrast causes severe performance degradation