Abstract
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.
Abstract (translated)
定位是一个突出的研究领域，特别是集中在视觉惯性导航（VIO）和同时定位与映射（SLAM）方法上。尽管这些方法取得了进步，但它们通常会遭遇死估计误差，导致在长时间穿越过程中估计平台运动的大幅偏差。在这种情况下，偏差误差不容忽视，应该得到纠正。我们提出的方法通过在每个时刻纠正由SLAM方法生成的估计运动来最小化偏差误差。我们的方法将定位测量由SLAM解决方案生成的随机变量视为多维分布中的随机变量。在这样一个设置中，偏差误差的纠正等同于找到这个多维分布中使关于平台运动和相关环境的几何先验的概率最大化的模式。我们的方法可以集成到任何SLAM/VIO方法中作为修正模块。我们的实验结果表明，通过我们的方法可以有效地将偏差误差降低10倍，在长穿越过程中。
URL
https://arxiv.org/abs/2404.10140
