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.

翻译：定位是研究的重点领域，尤其聚焦于视觉惯性里程计（VIO）及同步定位与地图构建（SLAM）方法。尽管这些方法取得了进展，但仍常面临航位推算误差问题，导致估计的平台运动产生显著漂移，尤其在长距离行进中。在此类情况下，漂移误差不可忽略，必须予以修正。我们提出的方法通过在每个时刻修正SLAM方法生成的估计运动来最小化漂移误差。该方法将SLAM解提供的定位测量值视为随机变量，并以多元联合分布形式建模。在此框架下，漂移校正等价于寻找该多元分布的众数，即联合最大化一组关于平台运动及环境的相关地理空间先验信息的似然值。该方法可作为校正模块集成至任意SLAM/VIO方法中。实验结果表明，所提方法在长距离行进中能将漂移误差降低10倍，效果显著。