Publikation
Joint Global ICP for Improved Automatic Alignment of Full Turn Object Scans
Torben Fetzer; Gerd Reis; Didier Stricker
In: International Conference on Computer Analysis of Images and Patterns. International Conference on Computer Analysis of Images and Patterns (CAIP-2021), September 28-30, Online, Springer LNCS, 2021.
Zusammenfassung
Point cloud registration is an important task in computer vision, computer
graphics, robotics, odometry and many other disciplines. The problem has
been studied for a long time and many different approaches have been established.
In the case of existing rough initializations, the ICP approach is still widely used
as the state of the art. Often only the pairwise problem is treated. In case of many
applications, especially in 3D reconstruction, closed rotations of sequences of
partial reconstructions have to be registered. We show that there are considerable
advantages if ICP iterations are performed jointly instead of the usual pairwise
approach (Pulli’s approach). Without the need for increased computational effort,
lower alignment errors are achieved, drift is avoided and calibration errors
are uniformly distributed over all scans. The joint approach is further extended
into a global version, which not only considers one-sided adjacent scans, but updates
symmetrically in both directions. The result is an approach that leads to a
much smoother and more stable convergence, which moreover enables a stable
stopping criterion to be applied. This makes the procedure fully automatic and
therefore superior to most other methods, that often tremble close to the optimum
and have to be terminated manually. We present a complete procedure, which in
addition addresses the issue of automatic outlier detection in order to solve