Communication constrained cloudbased longterm visual localization in real time Visual localization is one of the primary capabilities for mobile robots. Longterm visual localization in real time is particularly challenging, in which the robot is required to efficiently localize itself using visual data where appearance may change significantly over time. In this paper, we propose a cloudbased visual localization system targeting at longterm localization in real time. On the robot, we employ two estimators to achieve accurate and realtime performance. One is a slidingwindow based visual inertial odometry, which integrates constraints from consecutive observations and selfmotion measurements, as well as the constraints induced by localization results from the cloud. This estimator builds a local visual submap as the virtual observation which is then sent to the cloud as new localization constraints. The other one is a delayed state Extended Kalman Filter to fuse the pose of the robot localized from the cloud, the local odometry and the highfrequency inertial measurements. On the cloud, we propose a longer slidingwindow based localization method to aggregate the virtual observations for larger field of view, leading to more robust alignment between virtual observations and the map. Under this architecture, the robot can achieve driftfree and realtime localization using onboard resources even in a network with limited bandwidth, high latency and existence of package loss, which enables the autonomous navigation in realworld environment. We evaluate the effectiveness of our system on a dataset with challenging seasonal and illuminative variations. We further validate the robustness of the system under challenging network conditions.

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.