A Framework for Depth Estimation and Relative Localization of Ground Robots using Computer Vision The 3D depth estimation and relative pose estimation problem within a decentralized architecture is a challenging problem that arises in missions that require coordination among multiple visioncontrolled robots. The depth estimation problem aims at recovering the 3D information of the environment. The relative localization problem consists of estimating the relative pose between two robots, by sensing each otherxe2x80x99s pose or sharing information about the perceived environment. Most solutions for these problems use a set of discrete data without taking into account the chronological order of the events. This paper builds on recent results on continuous estimation to propose a framework that estimates the depth and relative pose between two nonholonomic vehicles. The basic idea consists in estimating the depth of the points by explicitly considering the dynamics of the camera mounted on a ground robot, and feeding the estimates of 3D points observed by both cameras in a filter that computes the relative pose between the robots. We evaluate the convergence for a set of simulated scenarios and show experimental results validating the proposed framework.

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.