一种面向视觉惯性SLAM的基于概率的漂移校正模块
A Probabilistic-based Drift Correction Module for Visual Inertial SLAMs
AI总结：
针对视觉惯性SLAM/VIO长距离行进中漂移误差大的问题，提出一种基于概率的漂移校正模块，将定位测量建模为多元分布并通过求解众数校正漂移，可集成到任意SLAM/VIO方法中，实验显示长距离下漂移误差降低10倍。
摘要：
定位是一个重要的研究领域，重点聚焦于视觉惯性里程计（Visual Inertial Odometry, VIO）与同时定位与地图构建（Simultaneous Localization and Mapping, SLAM）方法。尽管这些方法已取得进展，但它们常出现航位推算误差，尤其在长距离行进中会导致平台运动估计产生显著漂移，这类漂移误差不可忽略，必须予以校正。我们提出的方法通过在每个周期校正任意SLAM方法生成的运动估计来最小化漂移误差。该方法将SLAM方案输出的定位测量值视为随机变量，以多元分布联合建模。在此设定下，漂移校正等价于求解该多元分布的众数，即联合最大化一组与平台运动和环境相关的地理空间先验的似然。该方法可作为校正模块集成到任意SLAM/VIO方法中。实验结果表明，我们的方法在长距离行进中可将漂移误差降低10倍，验证了其有效性。
英文摘要：
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.
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