Ouerghi Safa, Boutteau Rémi, Savatier Xavier, Tlili Fethi
Carthage University, SUP'COM, GRESCOM, El Ghazela 2083, Tunisia.
Normandie University, UNIROUEN, ESIGELEC, IRSEEM, 76000 Rouen, France.
Sensors (Basel). 2018 Mar 22;18(4):939. doi: 10.3390/s18040939.
In this paper, we address the problem of vehicle localization in urban environments. We rely on visual odometry, calculating the incremental motion, to track the position of the vehicle and on place recognition to correct the accumulated drift of visual odometry, whenever a location is recognized. The algorithm used as a place recognition module is SeqSLAM, addressing challenging environments and achieving quite remarkable results. Specifically, we perform the long-term navigation of a vehicle based on the fusion of visual odometry and SeqSLAM. The template library for this latter is created online using navigation information from the visual odometry module. That is, when a location is recognized, the corresponding information is used as an observation of the filter. The fusion is done using the EKF and the UKF, the well-known nonlinear state estimation methods, to assess the superior alternative. The algorithm is evaluated using the KITTI dataset and the results show the reduction of the navigation errors by loop-closure detection. The overall position error of visual odometery with SeqSLAM is 0.22% of the trajectory, which is much smaller than the navigation errors of visual odometery alone 0.45%. In addition, despite the superiority of the UKF in a variety of estimation problems, our results indicate that the UKF performs as efficiently as the EKF at the expense of an additional computational overhead. This leads to the conclusion that the EKF is a better choice for fusing visual odometry and SeqSlam in a long-term navigation context.
在本文中，我们探讨城市环境中的车辆定位问题。我们依靠视觉里程计来计算增量运动，以跟踪车辆的位置，并依靠地点识别来校正视觉里程计累积的漂移，只要识别到某个位置。用作地点识别模块的算法是SeqSLAM，它能应对具有挑战性的环境并取得相当显著的成果。具体而言，我们基于视觉里程计和SeqSLAM的融合来执行车辆的长期导航。后者的模板库是使用来自视觉里程计模块的导航信息在线创建的。也就是说，当识别到一个位置时，相应的信息被用作滤波器的观测值。融合使用扩展卡尔曼滤波器（EKF）和无迹卡尔曼滤波器（UKF）这两种著名的非线性状态估计方法来评估哪种方法更优。该算法使用KITTI数据集进行评估，结果表明通过闭环检测减少了导航误差。结合SeqSLAM的视觉里程计的总体位置误差为轨迹的0.22%，这比单独的视觉里程计的导航误差0.45%小得多。此外，尽管UKF在各种估计问题中具有优势，但我们的结果表明，UKF在有额外计算开销的情况下与EKF的效率相当。由此得出结论，在长期导航背景下，EKF是融合视觉里程计和SeqSlam的更好选择。
