Perception System Design for LowCost Commercial Ground Robots: Sensor Configurations, Calibration, Localization and Mapping For commercially successful ground robots, high degree of autonomy, low manufacturing and maintenance cost, as well as minimized deployment limitations in different environments are essential attributes. To deliver an xe2x80x98anywhere deployablexe2x80x99 product, it is impractical to rely on one single sensor or one single piece of algorithm to overcome all related challenges. Instead, the entire robotic system should be dedicated designed, including the choices of sensors, processors, algorithm integration for various functionality, and so on.This paper presents our design of perception system for commercial ground robots, which is able to operate in most common environments. The designed system is equipped with lowcost sensors and processors. The first key contribution of this paper is the design of the robotic sensory system, which includes a monocular camera, a 2D laser range finder (LRF), wheel encoders, and an inertial measurement unit (IMU). Our sensory system can be built at a cost of as low as 100. Furthermore, the selected sensors provide complementary characteristics for perception of both robot egomotion and its surrounding environments, which are the prerequisites for xe2x80x98anywherexe2x80x99 deployment. The second key contribution of this paper is that a complete set of technologies is proposed based on our sensor systems, including sensor calibration (factory calibration and online calibration), localization (environmental exploring and relocalization), as well as mapping. The proposed methodology includes both efficient engineering implementation and theoretical novelty for high performance systems. Experimental results from our robotic testing platform and offtheshelf commercial robots are presented. These results demonstrate that the proposed system can be deployed in various environmental conditions without performance compromise.

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.