  Positioning is a prominent field of study, notably focusing on Visual
Inertial Odometry (VIO) and Simultaneous Localization and Mapping (SLAM)
methods. Despite their advancements, these methods often encounter
dead-reckoning errors that leads to considerable drift in estimated platform
motion especially during long traverses. In such cases, the drift error is not
negligible and should be rectified. Our proposed approach minimizes the drift
error by correcting the estimated motion generated by any SLAM method at each
epoch. Our methodology treats positioning measurements rendered by the SLAM
solution as random variables formulated jointly in a multivariate distribution.
In this setting, The correction of the drift becomes equivalent to finding the
mode of this multivariate distribution which jointly maximizes the likelihood
of a set of relevant geo-spatial priors about the platform motion and
environment. Our method is integrable into any SLAM/VIO method as an correction
module. Our experimental results shows the effectiveness of our approach in
minimizing the drift error by 10x in long treverses.
