  In computed tomography (CT), the relative trajectories of a sample, a
detector, and a signal source are traditionally considered to be known, since
they are caused by the intentional preprogrammed movement of the instrument
parts. However, due to the mechanical backlashes, rotation sensor measurement
errors, thermal deformations real trajectory differs from desired ones. This
negatively affects the resulting quality of tomographic reconstruction. Neither
the calibration nor preliminary adjustments of the device completely eliminates
the inaccuracy of the trajectory but significantly increase the cost of
instrument maintenance. A number of approaches to this problem are based on an
automatic refinement of the source and sensor position estimate relative to the
sample for each projection (at each time step) during the reconstruction
process. A similar problem of position refinement while observing different
images of an object from different angles is well known in robotics
(particularly, in mobile robots and self-driving vehicles) and is called
Simultaneous Localization And Mapping (SLAM). The scientific novelty of this
work is to consider the problem of trajectory refinement in microtomography as
a SLAM problem. This is achieved by extracting Speeded Up Robust Features
(SURF) features from X-ray projections, filtering matches with Random Sample
Consensus (RANSAC), calculating angles between projections, and using them in
factor graph in combination with stepper motor control signals in order to
refine rotation angles.
