A Robust Position and Posture Measurement System Using Visual Markers and an Inertia Measurement Unit Automatic control of mobile robots and robot arms requires techniques for estimating the position and orientation of objects with high accuracy and robustness. Although methods using markers or machine learning have been developed, generalpurpose and highly accurate estimations have not been realized. Our team developed a highaccuracy visual marker xe2x80x9cLentiMarkxe2x80x9d for highaccuracy estimations of position and posture, but data are lost when the camera cannot obtain marker images. We therefore developed the MarkerIMU system for integrating visual markers with an inertia measurement unit (IMU). When cameras cannot acquire the image of a visual marker, any missing data are restored from IMU data. However, when calculating positions from acceleration sensor values, sensor error increases estimation error. Therefore, we developed a method for error correction using measurements from before and after the missing data. Evaluation experiments confirm that missing data can be estimated using the proposed method.

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.