Vision-Aided Localization For Ground Robots

In this paper, we focus on the problem of vision-based localization for ground robotic applications. In recent years, camera only or camera-IMU (inertial measurement unit) based localization methods are widely studied, in terms of theoretical properties, algorithm design, and real-world applications. However, we experimentally find that none of existing methods is able to perform high-precision and robust localization for ground robots in large-scale complicated 3D environments. To this end, in this paper, we propose a novel vision-based 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 sliding-window 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 real-world experiments, the proposed algorithm outperforms a number of the state-of-the-art vision based localization algorithms by a significant margin, especially when deployed in large-scale complicated environments.