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.
Keywords: EKF; SeqSLAM; UKF; loop-closure; real-time navigation; visual-odometry.