OutlierRobust Manifold PreIntegration for INSGPS Fusion We tackle the INSGPS sensor fusion problem for pose estimation, particularly in the common setting where the INS components (IMU and magnetometer) function at much higher frequencies than GPS, and where the magnetometer and GPS are prone to giving erroneous measurements (outliers) due to magnetic disturbances and glitches. Our main contribution is a novel nonlinear optimization framework that (1) fuses preintegrated IMU and magnetometer measurements with GPS, in a manner that respects the manifold structure of the state space; and (2) supports the usage of robust norms and efficient large scale optimization to effectively mitigate the effects of outliers. Through extensive experiments, we demonstrate the superior accuracy and robustness of our approach over filtering methods (which are customarily applied in the target setting) with minimal impact to computational efficiency. Our work further illustrates the strength of optimization approaches in state estimation problems and paves the way for their adoption in the control and navigation communities.

VisionAided Localization For Ground Robots In this paper, we focus on the problem of visionbased localization for ground robotic applications. In recent years, camera only or cameraIMU (inertial measurement unit) based localization methods are widely studied, in terms of theoretical properties, algorithm design, and realworld applications. However, we experimentally find that none of existing methods is able to perform highprecision and robust localization for ground robots in largescale complicated 3D environments. To this end, in this paper, we propose a novel visionbased localization algorithm dedicatedly designed for ground robots, by fusing measurements from a camera, an IMU, and the wheel odometer. The first contribution of this paper is that we propose a novel algorithm for approximating the motion manifold for ground robots by parametric representation and performing pose integrating via IMU and wheel odometer measurements. Secondly, we propose a complete localization algorithm, by using a slidingwindow based estimator. The estimator is designed based on iterative optimization to fuse measurements from multiple sensors on the proposed manifold representation. We show that, based on a variety of realworld experiments, the proposed algorithm outperforms a number of the stateoftheart vision based localization algorithms by a significant margin, especially when deployed in largescale complicated environments.